Drug target prediction through deep learning functional representation of gene signatures

Hao Chen (), Frederick J. King, Bin Zhou, Yu Wang, Carter J. Canedy, Joel Hayashi, Yang Zhong, Max W. Chang, Lars Pache, Julian L. Wong, Yong Jia, John Joslin, Tao Jiang, Christopher Benner, Sumit K. Chanda and Yingyao Zhou ()
Additional contact information
Hao Chen: Novartis Biomedical Research
Frederick J. King: Novartis Biomedical Research
Bin Zhou: Novartis Biomedical Research
Yu Wang: Novartis Biomedical Research
Carter J. Canedy: Novartis Biomedical Research
Joel Hayashi: Novartis Biomedical Research
Yang Zhong: Novartis Biomedical Research
Max W. Chang: University of California, San Diego
Lars Pache: Sanford Burnham Prebys Medical Discovery Institute
Julian L. Wong: Novartis Biomedical Research
Yong Jia: Novartis Biomedical Research
John Joslin: Novartis Biomedical Research
Tao Jiang: University of California, Riverside
Christopher Benner: University of California, San Diego
Sumit K. Chanda: Scripps Research
Yingyao Zhou: Novartis Biomedical Research

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute’s L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Date: 2024
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DOI: 10.1038/s41467-024-46089-y

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