Robust, Generalizable, and Fair Machine Learning Models for Biomedicine

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Project Summary Modern machine learning approaches have attained substantial success in pattern recognition and high-dimensional data analyses. However, these algorithms heavily rely on association discovery, which cannot elucidate the mechanisms underpinning the observed correlations and suffers from limited generalizability. To address this challenge, the Yu Lab focuses on the development of robust and generalizable machine learning approaches to integrate various types of biomedical data, including multi-omics, pathology, and phenotypic information. The goal of the next five years is to develop novel computational methods that connect machine learning algorithms with causal inference methodologies to better understand the molecular mechanisms underpinning disease pathology and enable fair and robust predictions of drug response and toxicity. The overall vision of the proposed research program is to establish generalizable data-driven methods to transform biomedical data into robust prediction and mechanistic models. The proposed approach will systematically connect diverse biomedical signals to extract previously unknown knowledge on the molecular mechanisms and derive reliable prediction models for the effects of medications. The proposed approaches are innovative because they depart from the status quo by incorporating advanced causal inference techniques with data-driven algorithms to enhance mechanistic and predictive modeling. This research program is significant because it is expected to improve our understanding of disease pathology and provide a fair and generalizable informatics framework for drug response and adverse effects prediction in diverse populations. The proposed research activities will open new research horizons by establishing a new machine learning platform for generating reliable predictions, which will vertically advance molecular biology, pharmacology, and computational research in biomedicine.