Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, like viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become pandemics globally. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, like the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on Generative Adversarial Networks (GANs) which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles the real one, thus this generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform 3 distinct classification tasks by using 3 different sequence datasets (Influenza A Virus, PALMdb, VDjDB) and our results illustrate that GANs can improve the overall classification performance.
École normale supérieure（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；National Research University Higher School of Economics（俄罗斯国立高等经济学院）；LOB, École Polytechnique（光学与生物科学实验室，巴黎综合理工学院）；INSERM（法国国家健康与医学研究院）；Université Paris-Saclay（巴黎萨克雷大学）；INRIA（法国国家信息与自动化研究所）；ENS（巴黎高等师范学院）；HSE（俄罗斯国立高等经济学院）；University of Bristol（布里斯托大学）；Amazon（亚马逊）
Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories
Transforming Multi-Omics Integration with GANs: Applications in Alzheimer's and Cancer
Tulane Center for Biomedical Informatics（Tulane 生物医学信息学中心）；Genomics, Deming Department of Medicine, Tulane University（生物医学基因组学部， Tulane 大学医学部）；Department of Computer Science, Tulane University（计算机科学系， Tulane 大学）；Ochsner Center for Outcomes Research, Ochsner Research（Ochsner 研究中心， Ochsner 研究）
Experimental Quantum Generative Adversarial Networks for Image Generation
Hefei National Laboratory for Physical Sciences at the Microscale（合肥微尺度物质科学国家研究中心）；University of Science and Technology of China（中国科学技术大学）；CAS Center for Excellence in Quantum Information and Quantum Physics（中国科学院量子信息与量子物理卓越创新中心）；Shanghai Research Center for Quantum Sciences（上海量子科学研究中心）；Henan Key Laboratory of Quantum Information and Cryptography（河南省量子信息与密码学重点实验室）；University of Sydney（悉尼大学）
Exploring Diverse Methods in Visual Question Answering
University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Chongqing University（重庆大学）；Columbia University（哥伦比亚大学）；University of Chicago（芝加哥大学）
Exploring Major Transitions in the Evolution of Biological Cognition With Artificial Neural Networks
Institute for Human-Centered AI, Helmholtz Zentrum Munich（以人为中心的人工智能研究所，慕尼黑亥姆霍兹研究中心）；Leverhulme Centre for the Future of Intelligence, University of Cambridge（未来智能研究中心，剑桥大学）；School of Natural Sciences, Macquarie University（自然科学学院，麦考瑞大学）；School of Philosophy, The Australian National University（哲学学院，澳大利亚国立大学）；Department of History and Philosophy of Science, University of Cambridge（历史与哲学科学系，剑桥大学）；School of Environmental and Life Sciences, University of Hull（环境与生命科学学院，霍尔姆大学）；Centre for Data Science, AI, and Modelling, University of Hull（数据科学、人工智能与建模中心，霍尔姆大学）
Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data
Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Johannes Kepler University Linz（约翰·开普勒林茨大学）；Novo Nordisk A/S（诺和诺德公司）；University Hospital Basel（巴塞尔大学医院）；Imprint Labs, LLC.（Imprint Labs有限责任公司）
Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations
Marchuk Institute of Numerical Mathematics（马尔库克数值数学研究所）；Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University（洛蒙诺索夫莫斯科国立大学计算数学与 cybernetics 学院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究 institute (AIRI)）
