Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine largely due to small number of independent samples. In this paper, we show that neural networks can be trained to predict clinical outcomes using heterogeneous genomic data sources via multi-task learning and adversarial representation learning, allowing one to combine multiple cohorts and outcomes in training. We compare our proposed method to two baselines and demonstrate that it can be used to help mitigate the data scarcity and clinical outcome censorship in cancer genomics learning problems.
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
Stanford University（斯坦福大学）；University of Milano-Bicocca（米兰比可卡大学）
我们在这里介绍SIMLR（Single-cell Interpretation via Multi-kernel LeaRning），这是一个开源工具，它实现了一个新的框架，可以从观察到的异质样本的表达数据中学习样本间的相似性度量。SIMLR可以有效地用于执行诸如降维、聚类和异质样本群体的可视化等任务。SIMLR在几个公共数据集上针对这三个任务的最先进方法进行了基准测试，表明它是可扩展的，能够大大提高聚类性能，并通过更好的可视化使数据更具可解释性来提供有价值的见解。可用性和实施SIMLR在GitHub上的R和MATLAB实现中都可用。此外，它也可以作为R包在http://bioconductor.org上获得。
Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality
National Institutes of Health（美国国立卫生研究院）；National Institute on Aging（美国国家老龄化研究所）；University College London（伦敦大学学院）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）；UCL Movement Disorders Centre（伦敦大学学院运动障碍中心）；Data Tecnica International LLC（Data Tecnica国际有限责任公司）；German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病研究中心）；Khoury College of Computer Sciences, Northeastern University（东北大学卡尔计算机科学学院）；School of Nursing, Virginia Commonwealth University（弗吉尼亚联邦大学护理学院）；School of Pharmacy, Virginia Commonwealth University（弗吉尼亚联邦大学药学院）
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Medical College of Wisconsin（威斯康星医学院）；University of California, San Diego（加州大学圣地亚哥分校）；Baylor College of Medicine（贝勒医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of California, San Francisco（加州大学旧金山分校）
