# Robust, Generalizable, and Fair Machine Learning Models for Biomedicine

> **NIH NIH R35** · HARVARD MEDICAL SCHOOL · 2021 · $422,709

## Abstract

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.

## Key facts

- **NIH application ID:** 10275864
- **Project number:** 1R35GM142879-01
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Kun-Hsing Yu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $422,709
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10275864

## Citation

> US National Institutes of Health, RePORTER application 10275864, Robust, Generalizable, and Fair Machine Learning Models for Biomedicine (1R35GM142879-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10275864. Licensed CC0.

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