Author

Giorgio Bini

Italian Institute of Technology

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biochemistry· 2 Genetic & Genomic Medicine· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:GWAS summary statistics· 3 Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Gian Gaetano TartagliaiD· 5 shared Alexandros ArmaosiD· 2 shared Claudia GiambartolomeiiD· 2 shared Dimitrios Miltiadis VrachnosiD· 2 shared Gabriele Proietti· 2 shared George PaliourasiD· 2 shared Magdalena ArnaliD· 2 shared Adam S. ButterworthiD· 1 shared

Activity by Year

2022-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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5 shared papersGenetic & Genomic Medicine

Gian Gaetano TartagliaORCID

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Author profile →

5 papers

Recent·Most cited

Leveraging Large-Scale Biobanks for Therapeutic Target Discovery

Brian R. Ferolito, Hesam Dashti, Claudia Giambartolomei, Gina Marie Peloso, Daniel J Golden, Kai Gravel-Pucillo, Danielle Rasooly, Andréa R. V. R. Horimoto, Rachael Matty, Liam Gaziano, Yi Liu, Ines A. Smit, Barbara Zdrazil, Yakov A. Tsepilov, Lauren Costa, , Alexandre C. Pereira

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

Human Genetics and Genomics Advances · Dec 10, 2025Peer-reviewedGenetic & Genomic Medicine

Accurate Predictions of Phase Separating Proteins at Single Amino Acid Resolution

Michele Monti, Jonathan Fiorentino, Dimitrios Miltiadis Vrachnos, Giorgio Bini, Tiziana Cotrufo, Natalia Sánchez de Groot, Alexandros Armaos, Gian Gaetano Tartaglia

This paper introduces catGRANULE 2.0 ROBOT, an algorithm predicting protein LLPS at single amino acid resolution, showing high performance and experimental validation.

Genome biology · Feb 20, 2025Peer-reviewedBiochemistry

Decoding RNA-RNA Interactions: The Role of Low-Complexity Repeats and a Deep Learning Framework for Sequence-Based Prediction

Adriano Setti, Giorgio Bini, Flaminia Pellegrini, Valentino Maiorca, Gabriele Proietti, Dimitrios Miltiadis Vrachnos, Angelo D’Angelo, Alexandros Armaos, Julie Martone, Michele M. Monti, Giancarlo Ruocco, Emanuele Rodolà, Irene Bozzoni, Alessio Colantoni, Gian Gaetano Tartaglia

This paper identifies low-complexity repeats as key drivers of RNA-RNA interactions and introduces RIME, a deep learning model that outperforms traditional methods in predicting these interactions.

bioRxiv · Feb 16, 2025 · PreprintBiochemistry

Machine learning methods applied to classify complex diseases using genomic data

Magdalena Arnal, Giorgio Bini, Anastasia Krithara, George Paliouras, Gian Gaetano Tartaglia

This study compares machine learning methods for classifying multiple sclerosis and Alzheimer's disease using genomic data from UK Biobank, finding logistic regression is most robust while deep learning shows high variability.

bioRxiv · Mar 18, 2024 · PreprintBioinformatics

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

Magdalena Arnal, Giorgio Bini, Dietmar Fernández‐Orth, Eleftherios G. Samaras, Maya Kassis, Fotis Aisopos, Jordi Rambla, George Paliouras, Peter Garrard, Claudia Giambartolomei, Gian Gaetano Tartaglia

The paper uses machine learning to analyze genotyping data and identifies interactions between SNVs in late onset Alzheimer's disease, highlighting specific genomic profiles and interactions involving APOE and other genes.

Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · Jan 1, 2022Peer-reviewedGenetic & Genomic Medicine