Author

D. De Canditiis

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#machine learning Preprint Sep 2026

Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions

Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent approach that addresses this issue by combining neural networks with hierarchical sparsity constraints, enabling simultaneous prediction and variable selection. However, i...

D. De Canditiis, I. De Feis, P. Stolfi · 0 citations

Open access Jul 2026

scGraphVerse: a modular workflow for single-cell gene network inference

Abstract Motivation Inferring gene networks from single-cell RNA sequencing data is challenging due to high sparsity, dimensionality, and technical noise. Current pipelines lack the multi-dataset integration and comprehensive post-processing analysis. Results scGraphVerse is an R package that integrates multiple algori...

Francesco Cecere, D. De Canditiis, Annamaria Carissimo et al. · Bioinformatics Advances · 0 citations