Language
English
Publication Date
2-6-2025
Journal
npj Precision Oncology
DOI
10.1038/s41698-025-00825-9
PMID
39915634
PMCID
PMC11802771
PubMedCentral® Posted Date
2-6-2025
PubMedCentral® Full Text Version
Post-print
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 prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease.
Keywords
Cancer, Computational biology and bioinformatics, Biomarkers, Molecular medicine, Oncology
Published Open-Access
yes
Recommended Citation
Henke, David M; Renwick, Alexander; Zoeller, Joseph R; et al., "Bio-Primed Machine Learning To Enhance Discovery of Relevant Biomarkers" (2025). Faculty, Staff and Students Publications. 6558.
https://digitalcommons.library.tmc.edu/baylor_docs/6558