Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data. In our work, we focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients that can guide treatment decisions for a specific individual through training on small data. In fact, doctors and clinicians always address this problem by studying several interrelated clinical variables simultaneously. We attempt to simulate such clinical perspective, and introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks and transfer it to help address new tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification. Observing that gene expression data have specifically high dimensionality and high noise properties compared with image data, we proposed a new extension of it by appending two modules to address these issues. Concretely, we append a feature selection layer to automatically filter out the disease-irrelated genes and incorporate a sample reweighting strategy to adaptively remove noisy data, and meanwhile the extended model is capable of learning from a limited number of training examples and generalize well. Simulations and real gene expression data experiments substantiate the superiority of the proposed method for predicting the subtypes of disease and identifying potential disease-related genes.
TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes
Tianjin University（天津大学）；Northwestern Polytechnical University（西北工业大学）；La Trobe University（拉筹伯大学）；Shantou University（汕头大学）；Yangtze River Delta Research Institute of Northwestern Polytechnical University（西北工业大学长三角研究院）
Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands（埃因霍温麦斯特大学放射学与核医学系）；Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands（埃因霍温麦斯特大学流行病学系）；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院霍克斯研究所）
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Bioinformatics Center, Kyoto University（京都大学生物信息学中心）；SANKEN, Osaka University（大阪大学SANKEN（大阪大学蛋白质研究所））；National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究所）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
University of Pennsylvania（宾夕法尼亚大学）；University of Southern California（南加州大学）；Lausanne University Hospital (CHUV) and University of Lausanne（洛桑大学医院（CHUV）和洛桑大学）；University of Texas San Antonio Health Science Center（德克萨斯大学圣安东尼奥健康科学中心）；The University of Melbourne（墨尔本大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；National Institute on Aging, National Institutes of Health（美国国立卫生研究院国家老龄化研究所）；Johns Hopkins University（约翰霍普金斯大学）；University of Wisconsin（威斯康星大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；National Institute on Aging（国家老龄化研究所）
Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
University of Pittsburgh（匹兹堡大学）；Beijing Information Science & Technology University（北京信息科技大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Astrata Solutions（阿斯特拉塔解决方案公司）
Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science and Technology, Zhejiang University（浙江可感知智能系统重点实验室，计算机科学与技术学院，浙江大学）
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Bruno Kessler Institute（布鲁诺·凯斯勒研究所）；University of Trento（特伦托大学）；Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Institute for Next Generation Healthcare（下一代医疗保健研究所）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Azienda USL Centro “Santi”（圣蒂中央地方卫生局）；HK3 Lab（HK3实验室）
UnPaSt: unsupervised patient stratification by biclustering of omics data
University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑工业大学）；Netherlands Cancer Institute（荷兰癌症研究所）；ACMetric；University of Campinas（坎皮纳斯大学）；University of British Columbia（不列颠哥伦比亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Altius Institute for Biomedical Sciences（Altius生物医学科学研究所）；University of Liège（列日大学）；Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Simon Fraser University（西蒙弗雷泽大学）；Vancouver Prostate Centre（温哥华前列腺中心）；Leibniz Institute of Virology（莱布尼茨病毒学研究所）；University of Southern Denmark（南丹麦大学）
Engineering Spatial and Molecular Features from Cellular Niches to Inform Predictions of Inflammatory Bowel Disease
Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida（电气与计算机工程系，赫伯特·韦斯剧院工程学院，佛罗里达大学）；Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida（流行病学系，公共卫生与健康专业学院及医学院，佛罗里达大学）；Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida（药理学与转化研究系，药学院，佛罗里达大学）
Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
School of Computing Technologies RMIT University Melbourne VIC Australia；Department of Biological Sciences；Department of Computer Science \& Information Systems Birla Institute of Technology；Institute of Innovation, Science；Sustainability Federation University Australia Ballarat VIC Australia；RMIT University；Birla Institute of Technology；Federation University Australia
