Davide Chicco

This paper uses Hyperdimensional Computing to predict chemical compound toxicity efficiently, showing it can classify compounds as toxic or non-toxic with computational advantages over traditional methods.

The paper introduces a Vector-Symbolic Architecture for encoding viral pangenomes and evaluates its effectiveness in species classification, finding that a flat species-level model outperforms genus-level and hierarchical approaches.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.

BEHST improves genomic set enrichment analysis by linking genomic regions to gene annotations via chromatin interactions, enhancing accuracy for non-coding regions.

The paper describes using Segway software to annotate genomes with chromatin data from ChIP-seq experiments, providing a semi-automated workflow for interpretation.