Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

Abstract

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

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• DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
• Segmentation and genome annotation algorithms
• Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
• Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales
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• Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
• Black-box Model Inversion Attribute Inference Attacks on Classification Models
• SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
• Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
• Leveraging binding-site structure for drug discovery with point-cloud methods
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• Studying Limits of Explainability by Integrated Gradients for Gene Expression Models
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• Multi-typed Objects Multi-view Multi-instance Multi-label Learning