John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colorectal cancer (CRC) benefits from a multi-omics-based stratification in the context of survival. Our TCGA-based study employs targeted feature selection and unsupervised clustering to stratify patients based on disease-specific survival, identifying an event-free subgroup undetectable with unimodal data or established consensus molecular subtypes. An analysis of variance and gene set enrichment coupled with clinical characterisation of the clusters reveal findings that support multi-omics-driven precision medicine in CRC.
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robert F. Gordon, Edward A. MacNair, et al.
WSC 1985
Ruixiong Tian, Zhe Xiang, et al.
Qinghua Daxue Xuebao/Journal of Tsinghua University
Yi Zhou, Parikshit Ram, et al.
ICLR 2023