Constructing diagnostic signature of serum microRNAs using machine learning for early pan-cancer detection.

ABSTRACT:

Background

Cancer is a major public health concern and the second leading cause of death worldwide. Various studies have reported the use of serum microRNAs (miRNAs) as non-invasive biomarkers for cancer detection. However, large-scale pan-cancer studies based on serum miRNAs have been relatively scarce.

Methods

An optimized machine learning workflow, combining least absolute shrinkage and selection operator (LASSO) analyses, recursive feature elimination (RFE), and fourteen kinds of machine learning algorithms, was use to screen out candidate miRNAs from 2540 serum miRNAs and constructed a potent diagnostic signature (Cancer-related Serum miRNA Signatures) for pan-cancer detection, based on a serum miRNA expression dataset of 38,223 samples.

Result

Cancer-related Ser

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11224052 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature