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Learning chemical sensitivity reveals mechanisms of cellular response

Medicine and Health

Learning chemical sensitivity reveals mechanisms of cellular response

W. Connell, K. Garcia, et al.

Discover ChemProbe, an innovative deep learning model crafted by William Connell, Kristle Garcia, Hani Goodarzi, and Michael J. Keiser. This model adeptly predicts cellular sensitivity to molecular probes and drugs using transcriptomic and chemical structural data, paving the way for precise cancer treatments and deep insights into molecular mechanisms.

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Playback language: English
Abstract
This paper introduces ChemProbe, a deep learning model that predicts cellular sensitivity to molecular probes and drugs by integrating transcriptomic and chemical structural data. ChemProbe accurately predicts drug response in cancer cell lines, tumor samples, and retrospectively evaluates precision breast cancer treatment. Prospective validation in new cellular models confirms its accuracy. Model interpretation reveals transcriptomic features reflecting compound targets and protein network modules, identifying genes driving ferroptosis. ChemProbe is presented as an interpretable *in silico* screening tool for research into molecular mechanisms of chemical sensitivity.
Publisher
Communications Biology
Published On
Sep 15, 2024
Authors
William Connell, Kristle Garcia, Hani Goodarzi, Michael J. Keiser
Tags
ChemProbe
deep learning
cellular sensitivity
transcriptomic data
drug response
precision medicine
ferroptosis
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