Bio-primed machine learning to enhance discovery of relevant biomarkers.

ABSTRACT: Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioriti

SUBMITTER: Henke DM 

PROVIDER: S-EPMC11802771 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature