Refining molecular subtypes and risk stratification of ovarian cancer through multi‐omics consensus portfolio and machine learning
Jing Zhang,
Shanshan He,
Hongjun Ying
Abstract:Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classification system despite extensive research efforts. This study integrated five distinct multi‐omics datasets from eight multicentric cohorts, applying a combination of ten clustering algorithms and ninety‐nine machine learning models. This methodology has enabled us to refine the molecular subtyping of OC, leading to the development of a novel Consensus Machine Learning‐driven Signature (CMLS). Our analysis delineated … Show more
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