AI RESEARCH
Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis
arXiv CS.LG
•
ArXi:2604.00065v1 Announce Type: cross Multi-omic datasets offer opportunities for improved biomarker discovery in cancer research, but their high dimensionality and limited sample sizes make identifying compact and effective biomarker panels challenging. Feature selection in large-scale omics can be efficiently addressed by combining machine learning with genetic algorithms, which naturally multi-objective optimization of predictive accuracy and biomarker set size.