Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.24.550346v1?rss=1
Authors: Chakraborty, D., Gutierrez-Chakraborty, E. P., Rodriguez-Aguayo, C., Basagaoglu, H., Lopez-Berestein, G., Amero, P.
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, which is 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.
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