Diagnostics of ovarian cancer via metabolite analysis and machine learning.
Publication Title
Integr Biol (Camb)
Document Type
Article
Publication Date
4-11-2023
Keywords
california; santa monica; pni
Abstract
Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC
Area of Special Interest
Cancer
Area of Special Interest
Women & Children
Area of Special Interest
Neurosciences (Brain & Spine)
Specialty/Research Institute
Neurosciences
Specialty/Research Institute
Obstetrics & Gynecology
Specialty/Research Institute
Oncology
DOI
10.1093/intbio/zyad005
