[2604.00065] Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis

Quantitative Biology > Genomics arXiv:2604.00065 (q-bio) [Submitted on 31 Mar 2026] Title:Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis Authors:Luca Cattelani, Vittorio Fortino View a PDF of the paper titled Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis, by Luca Cattelani and 1 other authors View PDF HTML (experimental) Abstract: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 support multi-objective optimization of predictive accuracy and biomarker set size. However, genetic algorithms remain relatively underexplored for multi-omic feature selection, where most approaches concatenate all layers into a single feature space. To address this limitation, we introduce Sweeping*, a multi-view, multi-objective algorithm alternating between single- and multi-view optimization. It employs a nested single-view multi-objective optimizer, and for this study we use the genetic algorithm NSGA3-CHS. It first identifies informative biomarkers within each layer, then jointly evaluates cross-layer interactions; these multi-omic solutions guide the next single-view search. Through ...