1 citations · 1 across the 2 of their papers we have counts for
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…
q-bio.QM2024★ 1 cited
Quantitative in vitro to in vivo extrapolation for human toxicology and drug development
Luca Cattelani, Giusy del Giudice, Angela Serra +12
Traditional animal testing for toxicity is expensive, time consuming, ethically questioned, sometimes inaccurate because of the necessity to extrapolate from animal to human, and i…
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…