Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable AI.
bioRxiv
; 2023 Jul 26.
Article
en En
| MEDLINE
| ID: mdl-37546729
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. Summary:
Explainable 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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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
BioRxiv
Año:
2023
Tipo del documento:
Article