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- W4385287453 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.Explainable Artificial Intelligence is amenable to generating biologically relevant testable hypotheses despite their limitations due to explanations originating from post hoc realizations." @default.
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- W4385287453 date "2023-07-26" @default.
- W4385287453 modified "2023-10-18" @default.
- W4385287453 title "Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable AI" @default.
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- W4385287453 doi "https://doi.org/10.1101/2023.07.24.550346" @default.
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