Identification of biomarkers in gynecologic cancers: a machine learning approach for metabolomics.

ABSTRACT: This study aimed to identify the clinical metabolic markers associated with gynecologic cancer (GC) by comparing machine learning (ML) algorithms with orthogonal partial least squares-discriminant analysis (OPLS-DA). Untargeted metabolomic analysis was performed on plasma from 42 patients with GC (24 cervical cancer [CC], 9 endometrial cancer [EC], and 9 ovarian cancer [OC]) and 57 healthy female participants. GC and healthy control groups were classified using OPLS-DA and 8 ML algorithms. The ML algorithm with the best classification performance was used to assess CC, EC, and OC with healthy subjects, and metabolite candidates involved in each GC were selected. Upon comparing the classification model performance between the GC and control groups, random forest (RF) model displayed the bes

SUBMITTER: Cha S 

PROVIDER: S-EPMC12518212 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature