A Variational Lower Bound to Mitigate Batch Effect in
Molecular Representations
Name
wang-wangchy-sm-eecs-2025-thesis.pdf
Description
Thesis PDF
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5.82 MB
Format
Adobe PDF
Checksum (MD5)
0f04c5134b08b5fa105db778fa98ffea
Author(s)
Wang, Chenyu
Advisor(s)
Jaakkola, Tommi S.
Date Issued
February 2025
Publisher
Massachusetts Institute of Technology
Abstract
High-throughput drug screening – using cell imaging or gene expression measurements as readouts of drug effect – is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. Experiments on drug screening data reveal InfoCORE’s superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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