DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection.

ABSTRACT: Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional pro

SUBMITTER: Halner A 

PROVIDER: S-EPMC10165183 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature