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bioRxiv · 10.1101/2023.07.24.550346

Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable AI

Chakraborty, D.·Gutierrez-Chakraborty, E. P.·Rodriguez-Aguayo, C.·Basagaoglu, H.·Lopez-Berestein, G.·Amero, P.
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Abstract

Explainable Artificial Intelligence (XAI) enables a holistic understanding of the complex and nonlinear relationships between genes and prognostic outcomes of cancer patients. In this study, we focus on a distinct aspect of XAI - to generate accurate and biologically relevant hypotheses and provide a shorter and more creative path to advance medical research. We present an XAI-driven approach to discover otherwise unknown genetic biomarkers as potential therapeutic targets in high-grade serous ovarian cancer, evidenced by the discovery of IL27RA, which leads to reduced peritoneal metastases when knocked down in tumor-carrying mice given IL27-siRNA-DOPC nanoparticles. SummaryExplainable Artificial Intelligence is amenable to generating biologically relevant testable hypotheses despite their limitations due to explanations originating from post hoc realizations.

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Chakraborty, D., Gutierrez-Chakraborty, E. P., Rodriguez-Aguayo, C., Basagaoglu, H., Lopez-Berestein, G., Amero, P.. 2023-07-26. Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable AI. https://doi.org/10.1101/2023.07.24.550346

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