Klev Diamanti

The paper uses rule-based machine learning to identify gene expression subtypes in paediatric SLE, revealing gene networks associated with disease activity and immune pathways.

MetaFetcheR maps small compound data by linking metabolite information across databases, resolving inconsistencies, and improving data fetching efficiency.

R.ROSETTA is an interpretable machine learning framework that identifies biological semantics from omics data, revealing potential gene interdependencies in autism case-control studies.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.