Search results for: Al Warith Al Rushaidi

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Rushaidi</title> <meta name="description" content="Search results for: Al Warith Al Rushaidi"> <meta name="keywords" content="Al Warith Al Rushaidi"> <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="Al Warith Al Rushaidi"> <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> 1</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Al Warith Al Rushaidi</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</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 a 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 drugprotein 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 cutting edge 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">7</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div 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Search results for: Al Warith Al Rushaidi