3 papers
q-bio.GN2026
Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis
Luca Cattelani, Vittorio Fortino
Multi-omic datasets offer opportunities for improved biomarker discovery in cancer research, but their high dimensionality and limited sample sizes make identifying compact and eff…
cs.LG2024
Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Vandad Imani, Elaheh Moradi, Carlos Sevilla-Salcedo +2
Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Gen…
q-bio.QM2023
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection
Luca Cattelani, Vittorio Fortino
The challenge in biomarker discovery using machine learning from omics data lies in the abundance of molecular features but scarcity of samples. Most feature selection methods in m…