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type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.07919">arXiv:2410.07919</a> <span> [<a href="https://arxiv.org/pdf/2410.07919">pdf</a>, <a href="https://arxiv.org/format/2410.07919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> InstructBioMol: Advancing Biomolecule Understanding and Design Following Human Instructions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+X">Xiang Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+K">Keyan Ding</a>, <a href="/search/q-bio?searchtype=author&query=Lyu%2C+T">Tianwen Lyu</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yinuo Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiaotong Li</a>, <a href="/search/q-bio?searchtype=author&query=Xiang%2C+Z">Zhuoyi Xiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zeyuan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+M">Ming Qin</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+K">Kehua Feng</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jike Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Qiang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Huajun Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.07919v1-abstract-short" style="display: inline;"> Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology, and enzyme engineering. Recent breakthroughs in Artificial Intelligence (AI) have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between AI's computational power and res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07919v1-abstract-full').style.display = 'inline'; document.getElementById('2410.07919v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.07919v1-abstract-full" style="display: none;"> Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology, and enzyme engineering. Recent breakthroughs in Artificial Intelligence (AI) have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between AI's computational power and researchers' intuition, using natural language to align molecular complexity with human intentions. Large Language Models (LLMs) have shown potential to interpret human intentions, yet their application to biomolecular research remains nascent due to challenges including specialized knowledge requirements, multimodal data integration, and semantic alignment between natural language and biomolecules. To address these limitations, we present InstructBioMol, a novel LLM designed to bridge natural language and biomolecules through a comprehensive any-to-any alignment of natural language, molecules, and proteins. This model can integrate multimodal biomolecules as input, and enable researchers to articulate design goals in natural language, providing biomolecular outputs that meet precise biological needs. Experimental results demonstrate InstructBioMol can understand and design biomolecules following human instructions. Notably, it can generate drug molecules with a 10% improvement in binding affinity and design enzymes that achieve an ESP Score of 70.4, making it the only method to surpass the enzyme-substrate interaction threshold of 60.0 recommended by the ESP developer. This highlights its potential to transform real-world biomolecular research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07919v1-abstract-full').style.display = 'none'; document.getElementById('2410.07919v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.12587">arXiv:2312.12587</a> <span> [<a href="https://arxiv.org/pdf/2312.12587">pdf</a>, <a href="https://arxiv.org/format/2312.12587">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Vora%2C+N+R">Neel R Vora</a>, <a href="/search/q-bio?searchtype=author&query=Hajighasemi%2C+A">Amir Hajighasemi</a>, <a href="/search/q-bio?searchtype=author&query=Reynolds%2C+C+T">Cody T. Reynolds</a>, <a href="/search/q-bio?searchtype=author&query=Radmehr%2C+A">Amirmohammad Radmehr</a>, <a href="/search/q-bio?searchtype=author&query=Mohamed%2C+M">Mohamed Mohamed</a>, <a href="/search/q-bio?searchtype=author&query=Saurav%2C+J+R">Jillur Rahman Saurav</a>, <a href="/search/q-bio?searchtype=author&query=Aziz%2C+A">Abdul Aziz</a>, <a href="/search/q-bio?searchtype=author&query=Veerla%2C+J+P">Jai Prakash Veerla</a>, <a href="/search/q-bio?searchtype=author&query=Nasr%2C+M+S">Mohammad S Nasr</a>, <a href="/search/q-bio?searchtype=author&query=Lotspeich%2C+H">Hayden Lotspeich</a>, <a href="/search/q-bio?searchtype=author&query=Guttikonda%2C+P+S">Partha Sai Guttikonda</a>, <a href="/search/q-bio?searchtype=author&query=Pham%2C+T">Thuong Pham</a>, <a href="/search/q-bio?searchtype=author&query=Darji%2C+A">Aarti Darji</a>, <a href="/search/q-bio?searchtype=author&query=Malidarreh%2C+P+B">Parisa Boodaghi Malidarreh</a>, <a href="/search/q-bio?searchtype=author&query=Shang%2C+H+H">Helen H Shang</a>, <a href="/search/q-bio?searchtype=author&query=Harvey%2C+J">Jay Harvey</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+K">Kan Ding</a>, <a href="/search/q-bio?searchtype=author&query=Nguyen%2C+P">Phuc Nguyen</a>, <a href="/search/q-bio?searchtype=author&query=Luber%2C+J+M">Jacob M Luber</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.12587v2-abstract-short" style="display: inline;"> Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of battery-dependent physiological monit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.12587v2-abstract-full').style.display = 'inline'; document.getElementById('2312.12587v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.12587v2-abstract-full" style="display: none;"> Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of battery-dependent physiological monitoring wearables. This paper presents a novel deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:293 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the Hospital and deploy the solution on commonly used embedded AI chips (i.e., ARM Cortex V8 and Jetson Nano). The proposed framework achieves a 91% seizure detection accuracy using XGBoost, confirming the approach's reliability, practicality, and scalability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.12587v2-abstract-full').style.display = 'none'; document.getElementById('2312.12587v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03269">arXiv:2310.03269</a> <span> [<a href="https://arxiv.org/pdf/2310.03269">pdf</a>, <a href="https://arxiv.org/format/2310.03269">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> InstructProtein: Aligning Human and Protein Language via Knowledge Instruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zeyuan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Qiang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+K">Keyan Ding</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+M">Ming Qin</a>, <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+X">Xiang Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiaotong Li</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Huajun Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.03269v1-abstract-short" style="display: inline;"> Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03269v1-abstract-full').style.display = 'inline'; document.getElementById('2310.03269v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03269v1-abstract-full" style="display: none;"> Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation. To achieve this, we first pre-train an LLM on both protein and natural language corpora, enabling it to comprehend individual languages. Then supervised instruction tuning is employed to facilitate the alignment of these two distinct languages. Herein, we introduce a knowledge graph-based instruction generation framework to construct a high-quality instruction dataset, addressing annotation imbalance and instruction deficits in existing protein-text corpus. In particular, the instructions inherit the structural relations between proteins and function annotations in knowledge graphs, which empowers our model to engage in the causal modeling of protein functions, akin to the chain-of-thought processes in natural languages. Extensive experiments on bidirectional protein-text generation tasks show that InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover, InstructProtein serves as a pioneering step towards text-based protein function prediction and sequence design, effectively bridging the gap between protein and human language understanding. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03269v1-abstract-full').style.display = 'none'; document.getElementById('2310.03269v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.16780">arXiv:2306.16780</a> <span> [<a href="https://arxiv.org/pdf/2306.16780">pdf</a>, <a href="https://arxiv.org/format/2306.16780">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Graph Sampling-based Meta-Learning for Molecular Property Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+X">Xiang Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Qiang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+B">Bin Wu</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+K">Keyan Ding</a>, <a href="/search/q-bio?searchtype=author&query=Fang%2C+Y">Yin Fang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Huajun Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.16780v1-abstract-short" style="display: inline;"> Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-ba… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.16780v1-abstract-full').style.display = 'inline'; document.getElementById('2306.16780v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.16780v1-abstract-full" style="display: none;"> Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction. First, we construct a Molecule-Property relation Graph (MPG): molecule and properties are nodes, while property labels decide edges. Then, to utilize the topological information of MPG, we reformulate an episode in meta-learning as a subgraph of the MPG, containing a target property node, molecule nodes, and auxiliary property nodes. Third, as episodes in the form of subgraphs are no longer independent of each other, we propose to schedule the subgraph sampling process with a contrastive loss function, which considers the consistency and discrimination of subgraphs. Extensive experiments on 5 commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-art methods by 5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposed module. Our code is available at https://github.com/HICAI-ZJU/GS-Meta. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.16780v1-abstract-full').style.display = 'none'; document.getElementById('2306.16780v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by IJCAI 2023</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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