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prediction</title> <meta name="description" content="Search results for: drug prediction"> <meta name="keywords" content="drug prediction"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="drug prediction"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4194</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: drug prediction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4194</span> Drug-Drug Interaction Prediction in Diabetes Mellitus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashini%20Maduka">Rashini Maduka</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20R.%20Wijesinghe"> C. R. Wijesinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Weerasinghe"> A. R. Weerasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug-drug%20interaction%20prediction" title="drug-drug interaction prediction">drug-drug interaction prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20embedding" title=" graph embedding"> graph embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20convolutional%20networks" title=" graph convolutional networks"> graph convolutional networks</a>, <a href="https://publications.waset.org/abstracts/search?q=adverse%20drug%20effects" title=" adverse drug effects"> adverse drug effects</a> </p> <a href="https://publications.waset.org/abstracts/165305/drug-drug-interaction-prediction-in-diabetes-mellitus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165305.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4193</span> Proposing an Architecture for Drug Response Prediction by Integrating Multiomics Data and Utilizing Graph Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nishank%20Raisinghani">Nishank Raisinghani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20discovery" title="drug discovery">drug discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=transformers" title=" transformers"> transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20networks" title=" graph neural networks"> graph neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multiomics" title=" multiomics"> multiomics</a> </p> <a href="https://publications.waset.org/abstracts/169926/proposing-an-architecture-for-drug-response-prediction-by-integrating-multiomics-data-and-utilizing-graph-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169926.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4192</span> Using Combination of Sets of Features of Molecules for Aqueous Solubility Prediction: A Random Forest Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammet%20Baldan">Muhammet Baldan</a>, <a href="https://publications.waset.org/abstracts/search?q=Emel%20Timu%C3%A7in"> Emel Timuçin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solubility" title="solubility">solubility</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=maccs%20keys" title=" maccs keys"> maccs keys</a> </p> <a href="https://publications.waset.org/abstracts/186736/using-combination-of-sets-of-features-of-molecules-for-aqueous-solubility-prediction-a-random-forest-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186736.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4191</span> Graph Clustering Unveiled: ClusterSyn - A Machine Learning Framework for Predicting Anti-Cancer Drug Synergy Scores</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babak%20Bahri">Babak Bahri</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Yassaee%20Meybodi"> Fatemeh Yassaee Meybodi</a>, <a href="https://publications.waset.org/abstracts/search?q=Changiz%20Eslahchi"> Changiz Eslahchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20synergy" title="drug synergy">drug synergy</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning." title=" machine learning."> machine learning.</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/179301/graph-clustering-unveiled-clustersyn-a-machine-learning-framework-for-predicting-anti-cancer-drug-synergy-scores" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179301.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4190</span> Current Methods for Drug Property Prediction in the Real World</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jacob%20Green">Jacob Green</a>, <a href="https://publications.waset.org/abstracts/search?q=Cecilia%20Cabrera"> Cecilia Cabrera</a>, <a href="https://publications.waset.org/abstracts/search?q=Maximilian%20Jakobs"> Maximilian Jakobs</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Dimitracopoulos"> Andrea Dimitracopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20van%20der%20Wilk"> Mark van der Wilk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ryan%20Greenhalgh"> Ryan Greenhalgh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activity%20%28QSAR%29" title="activity (QSAR)">activity (QSAR)</a>, <a href="https://publications.waset.org/abstracts/search?q=ADMET" title=" ADMET"> ADMET</a>, <a href="https://publications.waset.org/abstracts/search?q=classical%20methods" title=" classical methods"> classical methods</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20property%20prediction" title=" drug property prediction"> drug property prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20study" title=" empirical study"> empirical study</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/169980/current-methods-for-drug-property-prediction-in-the-real-world" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169980.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4189</span> Legal Judgment Prediction through Indictments via Data Visualization in Chinese</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kuo-Chun%20Chien">Kuo-Chun Chien</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Hui%20Chang"> Chia-Hui Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ren-Der%20Sun"> Ren-Der Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=legal%20judgment%20prediction" title="legal judgment prediction">legal judgment prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=BERT" title=" BERT"> BERT</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20visualization" title=" data visualization"> data visualization</a> </p> <a href="https://publications.waset.org/abstracts/147895/legal-judgment-prediction-through-indictments-via-data-visualization-in-chinese" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147895.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4188</span> BiFormerDTA: Structural Embedding of Protein in Drug Target Affinity Prediction Using BiFormer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Baghaarabani">Leila Baghaarabani</a>, <a href="https://publications.waset.org/abstracts/search?q=Parvin%20Razzaghi"> Parvin Razzaghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mennatolla%20Magdy%20Mostafa"> Mennatolla Magdy Mostafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Albaqsami"> Ahmad Albaqsami</a>, <a href="https://publications.waset.org/abstracts/search?q=Al%20Warith%20Al%20Rushaidi"> Al Warith Al Rushaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Al%20Rawahi"> Masoud Al Rawahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the interaction between drugs and their molecular targets is pivotal for advancing drug development processes. Due to the time and cost limitations, computational approaches have emerged as an effective approach to drug-target interaction (DTI) prediction. Most of the introduced computational based approaches utilize the drug molecule and protein sequence as input. This study does not only utilize these inputs, it also introduces a protein representation developed using a masked protein language model. In this representation, for every individual amino acid residue within the protein sequence, there exists a corresponding probability distribution that indicates the likelihood of each amino acid being present at that particular position. Then, the similarity between each pair of amino-acids is computed to create similarity matrix. To encode the knowledge of the similarity matrix, Bi-Level Routing Attention (BiFormer) is utilized, which combines aspects of transformer-based models with protein sequence analysis and represents a significant advancement in the field of drug-protein interaction prediction. BiFormer has the ability to pinpoint the most effective regions of the protein sequence that are responsible for facilitating interactions between the protein and drugs, thereby enhancing the understanding of these critical interactions. Thus, it appears promising in its ability to capture the local structural relationship of the proteins by enhancing the understanding of how it contributes to drug protein interactions, thereby facilitating more accurate predictions. To evaluate the proposed method, it was tested on two widely recognized datasets: Davis and KIBA. A comprehensive series of experiments was conducted to illustrate its effectiveness in comparison to cuttingedge techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BiFormer" title="BiFormer">BiFormer</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20language%20processing" title=" protein language processing"> protein language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention%20mechanism" title=" self-attention mechanism"> self-attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=binding%20affinity" title=" binding affinity"> binding affinity</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20target%20interaction" title=" drug target interaction"> drug target interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20matrix" title=" similarity matrix"> similarity matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20masked%20representation" title=" protein masked representation"> protein masked representation</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20language%20model" title=" protein language model"> protein language model</a> </p> <a href="https://publications.waset.org/abstracts/194594/biformerdta-structural-embedding-of-protein-in-drug-target-affinity-prediction-using-biformer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194594.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">11</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4187</span> In Silico Studies on Selected Drug Targets for Combating Drug Resistance in Plasmodium Falcifarum </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepika%20Bhaskar">Deepika Bhaskar</a>, <a href="https://publications.waset.org/abstracts/search?q=Neena%20Wadehra"> Neena Wadehra</a>, <a href="https://publications.waset.org/abstracts/search?q=Megha%20Gulati"> Megha Gulati</a>, <a href="https://publications.waset.org/abstracts/search?q=Aruna%20Narula"> Aruna Narula</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Vishnu"> R. Vishnu</a>, <a href="https://publications.waset.org/abstracts/search?q=Gunjan%20Katyal"> Gunjan Katyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With drug resistance becoming widespread in Plasmodium falciparum infections, development of the alternative drugs is the desired strategy for prevention and cure of malaria. Three drug targets were selected to screen promising drug molecules from the GSK library of around 14000 molecules. Using an in silico structure-based drug designing approach, the differences in binding energies of the substrate and inhibitor were exploited between target sites of parasite and human to design a drug molecule against Plasmodium. The docking studies have shown several promising molecules from GSK library with more effective binding as compared to the already known inhibitors for the drug targets. Though stronger interaction has been shown by several molecules as compare to reference, few molecules have shown the potential as drug candidates though in vitro studies are required to validate the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plasmodium" title="plasmodium">plasmodium</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria" title=" malaria"> malaria</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20targets" title=" drug targets"> drug targets</a>, <a href="https://publications.waset.org/abstracts/search?q=in%20silico%20studies" title=" in silico studies"> in silico studies</a> </p> <a href="https://publications.waset.org/abstracts/24319/in-silico-studies-on-selected-drug-targets-for-combating-drug-resistance-in-plasmodium-falcifarum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">450</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4186</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4185</span> Potential Drug-Drug Interactions at a Referral Hematology-Oncology Ward in Iran: A Cross-Sectional Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20Ataei">Sara Ataei</a>, <a href="https://publications.waset.org/abstracts/search?q=Molouk%20Hadjibabaie"> Molouk Hadjibabaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Shirinsadat%20Badri"> Shirinsadat Badri</a>, <a href="https://publications.waset.org/abstracts/search?q=Amirhossein%20Moslehi"> Amirhossein Moslehi</a>, <a href="https://publications.waset.org/abstracts/search?q=Iman%20Karimzadeh"> Iman Karimzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ardeshir%20Ghavamzadeh"> Ardeshir Ghavamzadeh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: To assess the pattern and probable risk factors for moderate and major drug–drug interactions in a referral hematology-oncology ward in Iran. Methods: All patients admitted to hematology–oncology ward of Dr. Shariati Hospital during a 6-month period and received at least two anti-cancer or non-anti-cancer medications simultaneously were included. All being scheduled anti-cancer and non-anti-cancer medications both prescribed and administered during ward stay were considered for drug–drug interaction screening by Lexi-Interact On- Desktop software. Results: One hundred and eighty-five drug–drug interactions with moderate or major severity were detected from 83 patients. Most of drug–drug interactions (69.73 %) were classified as pharmacokinetics. Fluconazole (25.95 %) was the most commonly offending medication in drug–drug interactions. Interaction of sulfamethoxazole-trimethoprim with fluconazole was the most common drug–drug interaction (27.27 %). Vincristine with imatinib was the only identified interaction between two anti-cancer agents. The number of administered medications during ward stay was considered as an independent risk factor for developing a drug–drug interaction. Conclusions: Potential moderate or major drug–drug interactions occur frequently in patients with hematological malignancies or related diseases. Performing larger standard studies are required to assess the real clinical and economical effects of drug–drug interactions on patients with hematological and non-hematological malignancies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%E2%80%93drug%20interactions" title="drug–drug interactions">drug–drug interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=hematology%E2%80%93oncology%20ward" title=" hematology–oncology ward"> hematology–oncology ward</a>, <a href="https://publications.waset.org/abstracts/search?q=hematological%20malignancies" title=" hematological malignancies "> hematological malignancies </a> </p> <a href="https://publications.waset.org/abstracts/17983/potential-drug-drug-interactions-at-a-referral-hematology-oncology-ward-in-iran-a-cross-sectional-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17983.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4184</span> Drug Use Knowledge and Antimicrobial Drug Use Behavior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pimporn%20Thongmuang">Pimporn Thongmuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The import value of antimicrobial drugs reached approximately fifteen million Baht in 2010, considered as the highest import value of all modern drugs, and this value is rising every year. Antimicrobials are considered the hazardous drugs by the Ministry of Public Health. This research was conducted in order to investigate the past knowledge of drug use and Antimicrobial drug use behavior. A total of 757 students were selected as the samples out of a population of 1,800 students. This selected students had the experience of Antimicrobial drugs use a year ago. A questionnaire was utilized in this research. The findings put on the view that knowledge gained by the students about proper use of antimicrobial drugs was not brought into practice. This suggests that the education procedure regarding drug use needs adjustment. And therefore the findings of this research are expected to be utilized as guidelines for educating people about the proper use of antimicrobial drugs. At a broader perspective, correct drug use behavior of the public may potentially reduce drug cost of the Ministry of Public Health of Thailand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20use%20knowledge" title="drug use knowledge">drug use knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=antimicrobial%20drugs" title=" antimicrobial drugs"> antimicrobial drugs</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20use%20behavior" title=" drug use behavior"> drug use behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=drug" title=" drug"> drug</a> </p> <a href="https://publications.waset.org/abstracts/3900/drug-use-knowledge-and-antimicrobial-drug-use-behavior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3900.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4183</span> Grey Wolf Optimization Technique for Predictive Analysis of Products in E-Commerce: An Adaptive Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shital%20Suresh%20Borse">Shital Suresh Borse</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayalaxmi%20Kadroli"> Vijayalaxmi Kadroli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20analysis" title="prediction analysis">prediction analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20wolf%20optimization" title=" grey wolf optimization"> grey wolf optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/148039/grey-wolf-optimization-technique-for-predictive-analysis-of-products-in-e-commerce-an-adaptive-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148039.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4182</span> Automatic Flood Prediction Using Rainfall Runoff Model in Moravian-Silesian Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sir">B. Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Podhoranyi"> M. Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kuchar"> S. Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kocyan"> T. Kocyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood">flood</a>, <a href="https://publications.waset.org/abstracts/search?q=HEC-HMS" title=" HEC-HMS"> HEC-HMS</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=runoff" title=" runoff "> runoff </a> </p> <a href="https://publications.waset.org/abstracts/20151/automatic-flood-prediction-using-rainfall-runoff-model-in-moravian-silesian-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4181</span> Spray-Dried, Biodegradable, Drug-Loaded Microspheres for Use in the Treatment of Lung Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mazen%20AlGharsan">Mazen AlGharsan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objective: The Carbopol Microsphere of Linezolid, a drug used to treat lung disease (pulmonary disease), was prepared using Buchi B-90 nano spray-drier. Methods: Production yield, drug content, external morphology, particle size, and in vitro release pattern were performed. Results: The work was 79.35%, and the drug content was 66.84%. The surface of the particles was shriveled in shape, with particle size distribution with a mean diameter of 9.6 µm; the drug was released in a biphasic manner with an initial release of 25.2 ± 5.7% at 60 minutes. It later prolonged the release by 95.5 ± 2.5% up to 12 hours. Differential scanning calorimetry (DSC) revealed no change in the melting point of the formulation. Fourier-transform infrared (FT-IR) studies showed no polymer-drug interaction in the prepared nanoparticles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nanotechnology" title="nanotechnology">nanotechnology</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20delivery" title=" drug delivery"> drug delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=Linezolid" title=" Linezolid"> Linezolid</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20disease" title=" lung disease"> lung disease</a> </p> <a href="https://publications.waset.org/abstracts/193025/spray-dried-biodegradable-drug-loaded-microspheres-for-use-in-the-treatment-of-lung-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193025.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">13</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4180</span> Artificial Intelligence in Bioscience: The Next Frontier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parthiban%20Srinivasan">Parthiban Srinivasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20discovery" title=" drug discovery"> drug discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20informatics" title=" health informatics"> health informatics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity%20prediction" title=" toxicity prediction"> toxicity prediction</a> </p> <a href="https://publications.waset.org/abstracts/63245/artificial-intelligence-in-bioscience-the-next-frontier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63245.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4179</span> Role of Social Support in Drug Cessation among Male Addicts in the West of Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzad%20Jalilian">Farzad Jalilian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Mirzaei%20Alavijeh"> Mehdi Mirzaei Alavijeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Fazel%20Zinat%20Motlagh"> Fazel Zinat Motlagh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social support is an important benchmark of health for people in avoidance conditions. The main goal of this study was to determine the three kinds of social support (family, friend and other significant) to drug cessation among male addicts, in Kermanshah, the west of Iran. This cross-sectional study was conducted among 132 addicts, randomly selected to participate voluntarily in the study. Data were collected from conduct interviews based on standard questionnaire and analyzed by using SPSS-18 at 95% significance level. The majority of addicts were young (Mean: 30.4 years), and with little education. Opium (36.4%), Crack (21.2%), and Methamphetamine (12.9%) were the predominant drugs. Inabilities to reject the offer and having addict friends are the most often reasons for drug usage. Almost, 18.9% reported history of drug injection. 43.2% of the participants already did drug cessation at least once. Logistic regression showed the family support (OR = 1.110), age (OR = 1.106) and drug use initiation age (OR = 0.918) was predicting drug cessation. Our result showed; family support is a more important effect among types of social support in drug cessation. It seems that providing educational program to addict’s families for more support of patients at drug cessation can be beneficial. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20cessation" title="drug cessation">drug cessation</a>, <a href="https://publications.waset.org/abstracts/search?q=family%20support" title=" family support"> family support</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20use" title=" drug use"> drug use</a>, <a href="https://publications.waset.org/abstracts/search?q=initiation%20age" title=" initiation age"> initiation age</a> </p> <a href="https://publications.waset.org/abstracts/33735/role-of-social-support-in-drug-cessation-among-male-addicts-in-the-west-of-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33735.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">551</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4178</span> Functionalized Nanoparticles for Drug Delivery Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Temesgen%20Geremew">Temesgen Geremew</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Functionalized nanoparticles have emerged as a revolutionary platform for drug delivery, offering significant advantages over traditional methods. By strategically modifying their surface properties, these nanoparticles can be designed to target specific tissues and cells, significantly reducing off-target effects and enhancing therapeutic efficacy. This targeted approach allows for lower drug doses, minimizing systemic exposure and potential side effects. Additionally, functionalization enables controlled release of the encapsulated drug, improving drug stability and reducing the frequency of administration, leading to improved patient compliance. This work explores the immense potential of functionalized nanoparticles in revolutionizing drug delivery, addressing limitations associated with conventional therapies and paving the way for personalized medicine with precise and targeted treatment strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nanoparticles" title="nanoparticles">nanoparticles</a>, <a href="https://publications.waset.org/abstracts/search?q=drug" title=" drug"> drug</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomaterials" title=" nanomaterials"> nanomaterials</a>, <a href="https://publications.waset.org/abstracts/search?q=applications" title=" applications"> applications</a> </p> <a href="https://publications.waset.org/abstracts/183288/functionalized-nanoparticles-for-drug-delivery-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183288.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4177</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4176</span> Pharmaceutical Science and Development in Drug Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adegoke%20Yinka%20Adebayo">Adegoke Yinka Adebayo </a> </p> <p class="card-text"><strong>Abstract:</strong></p> An understanding of the critical product attributes that impact on in vivo performance is key to the production of safe and effective medicines. Thus, a key driver for our research is the development of new basic science and technology underpinning the development of new pharmaceutical products. Research includes the structure and properties of drugs and excipients, biopharmaceutical characterisation, pharmaceutical processing and technology and formulation and analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20discovery" title="drug discovery">drug discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20development" title=" drug development"> drug development</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20delivery" title=" drug delivery "> drug delivery </a> </p> <a href="https://publications.waset.org/abstracts/19017/pharmaceutical-science-and-development-in-drug-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">494</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4175</span> Optimized Preprocessing for Accurate and Efficient Bioassay Prediction with Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeff%20Clarine">Jeff Clarine</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Shyh%20Peng"> Chang-Shyh Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20Sang"> Daisy Sang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioassay" title="bioassay">bioassay</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20screen" title=" virtual screen"> virtual screen</a> </p> <a href="https://publications.waset.org/abstracts/77481/optimized-preprocessing-for-accurate-and-efficient-bioassay-prediction-with-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77481.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4174</span> In silico Subtractive Genomics Approach for Identification of Strain-Specific Putative Drug Targets among Hypothetical Proteins of Drug-Resistant Klebsiella pneumoniae Strain 825795-1</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umairah%20Natasya%20Binti%20Mohd%20Omeershffudin">Umairah Natasya Binti Mohd Omeershffudin</a>, <a href="https://publications.waset.org/abstracts/search?q=Suresh%20Kumar"> Suresh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Klebsiella pneumoniae, a Gram-negative enteric bacterium that causes nosocomial and urinary tract infections. Particular concern is the global emergence of multidrug-resistant (MDR) strains of Klebsiella pneumoniae. Characterization of antibiotic resistance determinants at the genomic level plays a critical role in understanding, and potentially controlling, the spread of multidrug-resistant (MDR) pathogens. In this study, drug-resistant Klebsiella pneumoniae strain 825795-1 was investigated with extensive computational approaches aimed at identifying novel drug targets among hypothetical proteins. We have analyzed 1099 hypothetical proteins available in genome. We have used in-silico genome subtraction methodology to design potential and pathogen-specific drug targets against Klebsiella pneumoniae. We employed bioinformatics tools to subtract the strain-specific paralogous and host-specific homologous sequences from the bacterial proteome. The sorted 645 proteins were further refined to identify the essential genes in the pathogenic bacterium using the database of essential genes (DEG). We found 135 unique essential proteins in the target proteome that could be utilized as novel targets to design newer drugs. Further, we identified 49 cytoplasmic protein as potential drug targets through sub-cellular localization prediction. Further, we investigated these proteins in the DrugBank databases, and 11 of the unique essential proteins showed druggability according to the FDA approved drug bank databases with diverse broad-spectrum property. The results of this study will facilitate discovery of new drugs against Klebsiella pneumoniae. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pneumonia" title="pneumonia">pneumonia</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20target" title=" drug target"> drug target</a>, <a href="https://publications.waset.org/abstracts/search?q=hypothetical%20protein" title=" hypothetical protein"> hypothetical protein</a>, <a href="https://publications.waset.org/abstracts/search?q=subtractive%20genomics" title=" subtractive genomics"> subtractive genomics</a> </p> <a href="https://publications.waset.org/abstracts/82108/in-silico-subtractive-genomics-approach-for-identification-of-strain-specific-putative-drug-targets-among-hypothetical-proteins-of-drug-resistant-klebsiella-pneumoniae-strain-825795-1" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4173</span> Spatial Relationship of Drug Smuggling Based on Geographic Information System Knowledge Discovery Using Decision Tree Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Niamkaeo">S. Niamkaeo</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Robert"> O. Robert</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Chaowalit"> O. Chaowalit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20smuggling" title=" drug smuggling"> drug smuggling</a>, <a href="https://publications.waset.org/abstracts/search?q=Geographic%20Information%20System" title=" Geographic Information System"> Geographic Information System</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS%20knowledge%20discovery" title=" GIS knowledge discovery"> GIS knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=rule-based%20system" title=" rule-based system"> rule-based system</a> </p> <a href="https://publications.waset.org/abstracts/99772/spatial-relationship-of-drug-smuggling-based-on-geographic-information-system-knowledge-discovery-using-decision-tree-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99772.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">169</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4172</span> Functionalized DOX Nanocapsules by Iron Oxide Nanoparticles for Targeted Drug Delivery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afsaneh%20Ghorbanzadeh">Afsaneh Ghorbanzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Afshin%20Farahbakhsh"> Afshin Farahbakhsh</a>, <a href="https://publications.waset.org/abstracts/search?q=Zakieh%20Bayat"> Zakieh Bayat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The drug capsulation was used for release and targeted delivery in determined time, place and temperature or pH. The DOX nanocapsules were used to reduce and to minimize the unwanted side effects of drug. In this paper, the encapsulation methods of doxorubicin (DOX) and the labeling it by the magnetic core of iron (Fe3O4) has been studied. The Fe3O4 was conjugated with DOX via hydrazine bond. The solution was capsuled by the sensitive polymer of heat or pH such as chitosan-g-poly (N-isopropylacrylamide-co-N,N-dimethylacrylamide), dextran-g-poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) and mPEG-G2.5 PAMAM by hydrazine bond. The drug release was very slow at temperatures lower than 380°C. There was a rapid and controlled drug release at temperatures higher than 380°C. According to experiments, the use mPEG-G2.5PAMAM is the best method of DOX nanocapsules synthesis, because in this method, the drug delivery time to certain place is lower than other methods and the percentage of released drug is higher. The synthesized magnetic carrier system has potential applications in magnetic drug-targeting delivery and magnetic resonance imaging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20carrier" title="drug carrier">drug carrier</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20release" title=" drug release"> drug release</a>, <a href="https://publications.waset.org/abstracts/search?q=doxorubicin" title=" doxorubicin"> doxorubicin</a>, <a href="https://publications.waset.org/abstracts/search?q=iron%20oxide%20NPs" title=" iron oxide NPs"> iron oxide NPs</a> </p> <a href="https://publications.waset.org/abstracts/9068/functionalized-dox-nanocapsules-by-iron-oxide-nanoparticles-for-targeted-drug-delivery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9068.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4171</span> Prevalence of Drug Injection among Male Prisoners in the West of Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzad%20Jalilian">Farzad Jalilian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Mirzaei%20Alavijeh"> Mehdi Mirzaei Alavijeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Substance addiction is one of the major worldwide problems that destroys economy, familial relationships, and the abuser’s career and has several side effects; in the meantime drug injection due to the possibility of shared use of syringes among drug users could have multiple complications to be followed. The purpose of this study was to determine the prevalence of drug injection among male prisoners in Kermanshah city, the west of Iran. Methods: In this cross-sectional study 615 male prisoners were randomly selected to participate voluntarily in the study. Participants filled out a writing self-report questionnaire. Data were analyzed by the SPSS software (ver. 21.0) at 95% significant level. Results: The mean age of respondents was 31.13 years [SD: 7.76]. Mean initiation age for drug use was 14.36 years (range, 9-34 years). Almost, 39.4 % reported a history of drug use before prison. Opium (33.2%) and crystal (27.1%) was the most used drug among prisoners. Furthermore, 9.3 % had a history of injection addiction. There was a significant correlation between age, crime type, marital status, economic status, unprotected sex and drug injection (P < 0.05). Conclusion: The low age of drug abuse and the prevalence of drug injection among offenders can be as a warning for responsible; in this regard, implementation of prevention programs to risky behavior and harm reduction among high-risk groups can follow useful results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=substance%20abuse" title="substance abuse">substance abuse</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20injection" title=" drug injection"> drug injection</a>, <a href="https://publications.waset.org/abstracts/search?q=prison" title=" prison"> prison</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran" title=" Iran"> Iran</a> </p> <a href="https://publications.waset.org/abstracts/33740/prevalence-of-drug-injection-among-male-prisoners-in-the-west-of-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4170</span> Abridging Pharmaceutical Analysis and Drug Discovery via LC-MS-TOF, NMR, in-silico Toxicity-Bioactivity Profiling for Therapeutic Purposing Zileuton Impurities: Need of Hour</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saurabh%20B.%20Ganorkar">Saurabh B. Ganorkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Atul%20A.%20Shirkhedkar"> Atul A. Shirkhedkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need for investigations protecting against toxic impurities though seems to be a primary requirement; the impurities which may prove non - toxic can be explored for their therapeutic potential if any to assist advanced drug discovery. The essential role of pharmaceutical analysis can thus be extended effectively to achieve it. The present study successfully achieved these objectives with characterization of major degradation products as impurities for Zileuton which has been used for to treat asthma since years. The forced degradation studies were performed to identify the potential degradation products using Ultra-fine Liquid-chromatography. Liquid-chromatography-Mass spectrometry (Time of Flight) and Proton Nuclear Magnetic Resonance Studies were utilized effectively to characterize the drug along with five major oxidative and hydrolytic degradation products (DP’s). The mass fragments were identified for Zileuton and path for the degradation was investigated. The characterized DP’s were subjected to In-Silico studies as XP Molecular Docking to compare the gain or loss in binding affinity with 5-Lipooxygenase enzyme. One of the impurity of was found to have the binding affinity more than the drug itself indicating for its potential to be more bioactive as better Antiasthmatic. The close structural resemblance has the ability to potentiate or reduce bioactivity and or toxicity. The chances of being active biologically at other sites cannot be denied and the same is achieved to some extent by predictions for probability of being active with Prediction of Activity Spectrum for Substances (PASS) The impurities found to be bio-active as Antineoplastic, Antiallergic, and inhibitors of Complement Factor D. The toxicological abilities as Ames-Mutagenicity, Carcinogenicity, Developmental Toxicity and Skin Irritancy were evaluated using Toxicity Prediction by Komputer Assisted Technology (TOPKAT). Two of the impurities were found to be non-toxic as compared to original drug Zileuton. As the drugs are purposed and repurposed effectively the impurities can also be; as they can have more binding affinity; less toxicity and better ability to be bio-active at other biological targets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UFLC" title="UFLC">UFLC</a>, <a href="https://publications.waset.org/abstracts/search?q=LC-MS-TOF" title=" LC-MS-TOF"> LC-MS-TOF</a>, <a href="https://publications.waset.org/abstracts/search?q=NMR" title=" NMR"> NMR</a>, <a href="https://publications.waset.org/abstracts/search?q=Zileuton" title=" Zileuton"> Zileuton</a>, <a href="https://publications.waset.org/abstracts/search?q=impurities" title=" impurities"> impurities</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity" title=" toxicity"> toxicity</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-activity" title=" bio-activity"> bio-activity</a> </p> <a href="https://publications.waset.org/abstracts/95105/abridging-pharmaceutical-analysis-and-drug-discovery-via-lc-ms-tof-nmr-in-silico-toxicity-bioactivity-profiling-for-therapeutic-purposing-zileuton-impurities-need-of-hour" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95105.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4169</span> Modeling Optimal Lipophilicity and Drug Performance in Ligand-Receptor Interactions: A Machine Learning Approach to Drug Discovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jay%20Ananth">Jay Ananth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The drug discovery process currently requires numerous years of clinical testing as well as money just for a single drug to earn FDA approval. For drugs that even make it this far in the process, there is a very slim chance of receiving FDA approval, resulting in detrimental hurdles to drug accessibility. To minimize these inefficiencies, numerous studies have implemented computational methods, although few computational investigations have focused on a crucial feature of drugs: lipophilicity. Lipophilicity is a physical attribute of a compound that measures its solubility in lipids and is a determinant of drug efficacy. This project leverages Artificial Intelligence to predict the impact of a drug’s lipophilicity on its performance by accounting for factors such as binding affinity and toxicity. The model predicted lipophilicity and binding affinity in the validation set with very high R² scores of 0.921 and 0.788, respectively, while also being applicable to a variety of target receptors. The results expressed a strong positive correlation between lipophilicity and both binding affinity and toxicity. The model helps in both drug development and discovery, providing every pharmaceutical company with recommended lipophilicity levels for drug candidates as well as a rapid assessment of early-stage drugs prior to any testing, eliminating significant amounts of time and resources currently restricting drug accessibility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20discovery" title="drug discovery">drug discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=lipophilicity" title=" lipophilicity"> lipophilicity</a>, <a href="https://publications.waset.org/abstracts/search?q=ligand-receptor%20interactions" title=" ligand-receptor interactions"> ligand-receptor interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20development" title=" drug development"> drug development</a> </p> <a href="https://publications.waset.org/abstracts/163127/modeling-optimal-lipophilicity-and-drug-performance-in-ligand-receptor-interactions-a-machine-learning-approach-to-drug-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163127.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4168</span> On Improving Breast Cancer Prediction Using GRNN-CP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kefaya%20Qaddoum">Kefaya Qaddoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=conformal%20prediction" title=" conformal prediction"> conformal prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/74483/on-improving-breast-cancer-prediction-using-grnn-cp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74483.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4167</span> Detection of Important Biological Elements in Drug-Drug Interaction Occurrence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Ferdousi">Reza Ferdousi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Safdari"> Reza Safdari</a>, <a href="https://publications.waset.org/abstracts/search?q=Yadollah%20Omidi"> Yadollah Omidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drug-drug interactions (DDIs) are main cause of the adverse drug reactions and nature of the functional and molecular complexity of drugs behavior in human body make them hard to prevent and treat. With the aid of new technologies derived from mathematical and computational science the DDIs problems can be addressed with minimum cost and efforts. Market basket analysis is known as powerful method to identify co-occurrence of thing to discover patterns and frequency of the elements. In this research, we used market basket analysis to identify important bio-elements in DDIs occurrence. For this, we collected all known DDIs from DrugBank. The obtained data were analyzed by market basket analysis method. We investigated all drug-enzyme, drug-carrier, drug-transporter and drug-target associations. To determine the importance of the extracted bio-elements, extracted rules were evaluated in terms of confidence and support. Market basket analysis of the over 45,000 known DDIs reveals more than 300 important rules that can be used to identify DDIs, CYP 450 family were the most frequent shared bio-elements. We applied extracted rules over 2,000,000 unknown drug pairs that lead to discovery of more than 200,000 potential DDIs. Analysis of the underlying reason behind the DDI phenomena can help to predict and prevent DDI occurrence. Ranking of the extracted rules based on strangeness of them can be a supportive tool to predict the outcome of an unknown DDI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug-drug%20interaction" title="drug-drug interaction">drug-drug interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20basket%20analysis" title=" market basket analysis"> market basket analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20discovery" title=" rule discovery"> rule discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=important%20bio-elements" title=" important bio-elements"> important bio-elements</a> </p> <a href="https://publications.waset.org/abstracts/78955/detection-of-important-biological-elements-in-drug-drug-interaction-occurrence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78955.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4166</span> Effect of Alginate and Surfactant on Physical Properties of Oil Entrapped Alginate Bead Formulation of Curcumin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arpa%20Petchsomrit">Arpa Petchsomrit</a>, <a href="https://publications.waset.org/abstracts/search?q=Namfa%20Sermkaew"> Namfa Sermkaew</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruedeekorn%20Wiwattanapatapee"> Ruedeekorn Wiwattanapatapee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Oil entrapped floating alginate beads of curcumin were developed and characterized. Cremophor EL, Cremophor RH and Tween 80 were utilized to improve the solubility of the drug. The oil-loaded floating gel beads prepared by emulsion gelation method contained sodium alginate, mineral oil and surfactant. The drug content and % encapsulation declined as the ratio of surfactant was increased. The release of curcumin from 1% alginate beads was significantly more than for the 2% alginate beads. The drug released from the beads containing 25% of tween 80 was about 70% while a higher drug release was observed with the beads containing Cremophor EL or Cremohor RH (approximately 90%). The developed floating beads of curcumin powder with surfactant provided a superior drug release than those without surfactant. Floating beads based on oil entrapment containing the drug solubilized in surfactants is a new delivery system to enhance the dissolution of poorly soluble drugs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alginate" title="alginate">alginate</a>, <a href="https://publications.waset.org/abstracts/search?q=curcumin" title=" curcumin"> curcumin</a>, <a href="https://publications.waset.org/abstracts/search?q=floating%20drug%20delivery" title=" floating drug delivery"> floating drug delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20entrapped%20bead" title=" oil entrapped bead"> oil entrapped bead</a> </p> <a href="https://publications.waset.org/abstracts/3633/effect-of-alginate-and-surfactant-on-physical-properties-of-oil-entrapped-alginate-bead-formulation-of-curcumin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3633.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4165</span> An In-silico Pharmacophore-Based Anti-Viral Drug Development for Hepatitis C Virus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Romasa%20Qasim">Romasa Qasim</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20M.%20Sayedur%20Rahman"> G. M. Sayedur Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Nahid%20Hasan"> Nahid Hasan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Shazzad%20Hosain"> M. Shazzad Hosain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Millions of people worldwide suffer from Hepatitis C, one of the fatal diseases. Interferon (IFN) and ribavirin are the available treatments for patients with Hepatitis C, but these treatments have their own side-effects. Our research focused on the development of an orally taken small molecule drug targeting the proteins in Hepatitis C Virus (HCV), which has lesser side effects. Our current study aims to the Pharmacophore based drug development of a specific small molecule anti-viral drug for Hepatitis C Virus (HCV). Drug designing using lab experimentation is not only costly but also it takes a lot of time to conduct such experimentation. Instead in this in silico study, we have used computer-aided techniques to propose a Pharmacophore-based anti-viral drug specific for the protein domains of the polyprotein present in the Hepatitis C Virus. This study has used homology modeling and ab initio modeling for protein 3D structure generation followed by pocket identification in the proteins. Drug-able ligands for the pockets were designed using de novo drug design method. For ligand design, pocket geometry is taken into account. Out of several generated ligands, a new Pharmacophore is proposed, specific for each of the protein domains of HCV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pharmacophore-based%20drug%20design" title="pharmacophore-based drug design">pharmacophore-based drug design</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-viral%20drug" title=" anti-viral drug"> anti-viral drug</a>, <a href="https://publications.waset.org/abstracts/search?q=in-silico%20drug%20design" title=" in-silico drug design"> in-silico drug design</a>, <a href="https://publications.waset.org/abstracts/search?q=Hepatitis%20C%20virus%20%28HCV%29" title=" Hepatitis C virus (HCV)"> Hepatitis C virus (HCV)</a> </p> <a href="https://publications.waset.org/abstracts/64266/an-in-silico-pharmacophore-based-anti-viral-drug-development-for-hepatitis-c-virus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64266.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">271</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drug%20prediction&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drug%20prediction&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drug%20prediction&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drug%20prediction&page=5">5</a></li> <li class="page-item"><a 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