Removing Biases from Molecular Representations via Information Maximization

• URL: http://arxiv.org/abs/2312.00718v1
• Date: Fri, 1 Dec 2023 16:53:15 GMT
• Title: Removing Biases from Molecular Representations via Information Maximization
• Authors: Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi Jaakkola
• Abstract summary: InfoCORE is an Information approach for COnfounder REmoval to deal with batch effects. It adaptively reweighs samples to equalize their implied batch distribution. It offers a versatile framework and resolves general distribution shifts and issues of data fairness.
• Score: 16.38589836748167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. It adaptively reweighs samples to equalize their implied batch distribution. Extensive 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. The code is available at https://github.com/uhlerlab/InfoCORE.

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