Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods.

ABSTRACT: Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pan-cancer samples were concatenated and non-linearly projected to a lower dimension using an ML algorithm. This data was then clustered to identify multi-omics-based novel subgroups. The clinical chara

SUBMITTER: Khadirnaikar S 

PROVIDER: S-EPMC10586677 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature