| Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes JT Reese, H Blau, E Casiraghi, T Bergquist, JJ Loomba, TJ Callahan, ... EBioMedicine 87, 2023 | 92 | 2023 |
| KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response JT Reese, D Unni, TJ Callahan, L Cappelletti, V Ravanmehr, S Carbon, ... Patterns, 100155, 2020 | 91 | 2020 |
| Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments E Casiraghi, D Malchiodi, G Trucco, M Frasca, L Cappelletti, T Fontana, ... Ieee Access 8, 196299-196325, 2020 | 81 | 2020 |
| NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study JT Reese, B Coleman, L Chan, H Blau, TJ Callahan, L Cappelletti, ... Virology journal 19 (1), 84, 2022 | 35 | 2022 |
| GRAPE for fast and scalable graph processing and random-walk-based embedding L Cappelletti, T Fontana, E Casiraghi, V Ravanmehr, TJ Callahan, ... Nature Computational Science 3 (6), 552-568, 2023 | 17 | 2023 |
| On the quality of classification models for inferring ABAC policies from access logs L Cappelletti, S Valtolina, G Valentini, M Mesiti, E Bertino 2019 IEEE International Conference on Big Data (Big Data), 4000-4007, 2019 | 16 | 2019 |
| Complex data imputation by auto-encoders and convolutional neural networks—A case study on genome gap-filling L Cappelletti, T Fontana, GW Di Donato, L Di Tucci, E Casiraghi, ... Computers 9 (2), 37, 2020 | 15 | 2020 |
| Cyclooxygenase inhibitor use is associated with increased COVID-19 severity J Reese, B Coleman, L Chan, JC Tiffany, L Cappelletti, T Fontana, ... | 13 | 2021 |
| parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants A Petrini, M Mesiti, M Schubach, M Frasca, D Danis, M Re, G Grossi, ... GigaScience 9 (5), giaa052, 2020 | 13 | 2020 |
| Semi-automatic Column Type Inference for CSV Table Understanding S Bonfitto, L Cappelletti, F Trovato, G Valentini, M Mesiti SOFSEM 2021: Theory and Practice of Computer Science 12607, 535--549, 2021 | 11 | 2021 |
| The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species TE Putman, K Schaper, N Matentzoglu, VP Rubinetti, FS Alquaddoomi, ... Nucleic acids research 52 (D1), D938-D949, 2024 | 9 | 2024 |
| GraPE: fast and scalable Graph Processing and Embedding L Cappelletti, T Fontana, E Casiraghi, V Ravanmehr, T Callahan, ... | 8 | 2022 |
| Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer V Ravanmehr, H Blau, L Cappelletti, T Fontana, L Carmody, B Coleman, ... NAR Genomics and Bioinformatics 3 (4), lqab113, 2021 | 8 | 2021 |
| An open source knowledge graph ecosystem for the life sciences TJ Callahan, IJ Tripodi, AL Stefanski, L Cappelletti, SB Taneja, JM Wyrwa, ... Scientific Data 11 (1), 363, 2024 | 7 | 2024 |
| KG-Hub—building and exchanging biological knowledge graphs JH Caufield, T Putman, K Schaper, DR Unni, H Hegde, TJ Callahan, ... Bioinformatics 39 (7), btad418, 2023 | 7 | 2023 |
| KG-Microbe: A Reference Knowledge-Graph and Platform for Harmonized Microbial Information. MP Joachimiak, H Hegde, WD Duncan, JT Reese, L Cappelletti, ... ICBO, 131-133, 2021 | 7 | 2021 |
| Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding G Valentini, E Casiraghi, L Cappelletti, T Fontana, J Reese, P Robinson arXiv preprint arXiv:2101.01425, 2021 | 6 | 2021 |
| Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques L Cappelletti, A Petrini, J Gliozzo, E Casiraghi, M Schubach, M Kircher, ... BMC bioinformatics 23 (Suppl 2), 154, 2022 | 3 | 2022 |
| Bayesian optimization improves tissue-specific prediction of active regulatory regions with deep neural networks L Cappelletti, A Petrini, J Gliozzo, E Casiraghi, M Schubach, M Kircher, ... Bioinformatics and Biomedical Engineering: 8th International Work-Conference …, 2020 | 2 | 2020 |
| A neural model for the prediction of pathogenic genomic variants in Mendelian diseases A Cuzzocrea, L Cappelletti, G Valentini Proceedings of the 1st International Conference on Advances in Signal …, 2019 | 2 | 2019 |
Giorgio ValentiniComputer Science Dept. - University of MilanVerified email at di.unimi.it
