Author

Piero Ricchiuto

ORCID 0000-0002-8525-6624Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Ayan ChatterjeeiD· 3 shared Babak RavandiiD· 3 shared Juan Carlos MobareciD· 3 shared Parham HaddadiiD· 3 shared Tina Eliassi‐RadiD· 3 shared William R. MowreyiD· 3 shared Gabriel Tarrason RisaiD· 2 shared Khanshan FaribaiD· 2 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersSystems Biology

Ayan ChatterjeeORCID

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3 papers

Recent·Most cited

Clinical Trial and Ontology-Derived Positive and Negative Benchmark Datasets for Drug Repurposing Across Rare Diseases

Babak Ravandi, William R. Mowrey, Ayan Chatterjee, Khanshan Fariba, Parham Haddadi, Juan Carlos Mobarec, Simon Lambden, Tina Eliassi‐Rad, Piero Ricchiuto, Gabriel Tarrason Risa

The paper introduces two benchmark datasets for drug repurposing across rare diseases: a positive dataset from clinical trials and a negative dataset from disease ontologies, enabling evaluation of computational models for disease-drug associations.

bioRxiv · Jul 8, 2026 · PreprintSystems Biology

Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction

Ayan Chatterjee, Babak Ravandi, Parham Haddadi, Naomi H. Philip, Mario Abdelmessih, William R. Mowrey, Piero Ricchiuto, Yupu Liang, Wei Ding, Juan Carlos Mobarec, Tina Eliassi‐Rad

This paper introduces a method to improve protein-protein interaction prediction by using topology-driven negative sampling and an ML pipeline that enhances generalizability and interpretability.

Bioinformatics · Apr 7, 2025Peer-reviewedBioinformatics

Representation Learning of Human Diseases for Indication Expansion and Investment Decisions

Babak Ravandi, William R. Mowrey, Ayan Chatterjee, Fariba Khanshan, Parham Haddadi, Michaël Ughetto, Ian P. Barrett, Wei Ding, Guillermo del Angel, Tom Diethe, Juan Carlos Mobarec, Simon Lambden, Seng Cheng, Tina Eliassi‐Rad, Gabriel Tarrason Risa, Piero Ricchiuto

This paper introduces Dis2Vec, a representation learning framework for human diseases that enables therapeutic transferability prediction and disease clustering, enhancing translational research efficiency.

bioRxiv · Nov 19, 2024 · PreprintSystems Biology