Technology Category
Machine Learning: Multi-instance/Multi-view LearningSearch and Optimization: Algorithm ConfigurationConstraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction
Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis
🤖 AI Summary
This study addresses the challenge of identifying compact and effective prognostic biomarkers from high-dimensional multi-omics data under limited sample sizes. To this end, the authors propose Sweeping*, an algorithm that innovatively employs a multi-view alternating optimization framework. By iteratively performing multi-objective optimization—guided by the concordance index and root sparsity—between single-view and multi-view representations, Sweeping* simultaneously selects key omics features and models cross-omics interactions, thereby avoiding information loss inherent in naive concatenation strategies. Leveraging the NSGA-III-CHS genetic algorithm, five-fold cross-validation, and Pareto front analysis, Sweeping* demonstrates superior trade-offs between predictive accuracy and model complexity compared to clinical baselines across three TCGA cohorts, validating both the efficacy of multi-omics integration and its cohort-specific dependencies.
Technology Category
Application Category
📝 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 repeated sweeps, the algorithm progressively identifies compact biomarker panels capturing cross-modal complementary signals. We benchmark five Sweeping* strategies, including hierarchical and concatenation-based variants, using survival prediction on three TCGA cohorts. Each strategy jointly optimizes predictive accuracy and set size, measured via the concordance index and root-leanness. Overall performance and estimation error are assessed through cross hypervolume and Pareto delta under 5-fold cross-validation. Our results show that Sweeping* can improve the accuracy-complexity trade-off when sufficient survival signal is present and that integrating omic layers can enhance survival prediction beyond clinical-only models, although benefits remain cohort-dependent.
Problem
Research questions and friction points this paper is trying to address.
multi-omic
feature selection
cancer survival analysis
biomarker discovery
high dimensionality
Innovation
Methods, ideas, or system contributions that make the work stand out.
genetic algorithm
multi-omic feature selection
multi-view optimization
NSGA3-CHS
biomarker panel
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