CAPPELLETTI, LUCA
CAPPELLETTI, LUCA
Dipartimento di Informatica Giovanni Degli Antoni
Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning
2024 L. Cappelletti, L. Rekerle, T. Fontana, P. Hansen, E. Casiraghi, V. Ravanmehr, C.J. Mungall, J. Yang, L. Spranger, G. Karlebach, J.H. Caufield, L. Carmody, B. Coleman, T. Oprea, J. Reese, G. Valentini, P.N. Robinson
An open source knowledge graph ecosystem for the life sciences
2024 T.J. Callahan, I.J. Tripodi, A.L. Stefanski, L. Cappelletti, S.B. Taneja, J.M. Wyrwa, E. Casiraghi, N.A. Matentzoglu, J. Reese, J.C. Silverstein, C.T. Hoyt, R.D. Boyce, S.A. Malec, D.R. Unni, M.P. Joachimiak, P.N. Robinson, C.J. Mungall, E. Cavalleri, T. Fontana, G. Valentini, M. Mesiti, L.A. Gillenwater, B. Santangelo, N.A. Vasilevsky, R. Hoehndorf, T.D. Bennett, P.B. Ryan, G. Hripcsak, M.G. Kahn, M. Bada, W.A. Baumgartner, L.E. Hunter
SCALABLE GRAPH REPRESENTATIONAL LEARNING ALGORITHMS FOR NETWORK MEDICINE
2023 L. Cappelletti
Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes
2023 J.T. Reese, H. Blau, E. Casiraghi, T. Bergquist, J.J. Loomba, T.J. Callahan, B. Laraway, C. Antonescu, B. Coleman, M. Gargano, K.J. Wilkins, L. Cappelletti, T. Fontana, N. Ammar, B. Antony, T.M. Murali, J.H. Caufield, G. Karlebach, J.A. Mcmurry, A. Williams, R. Moffitt, J. Banerjee, A.E. Solomonides, H. Davis, K. Kostka, G. Valentini, D. Sahner, C.G. Chute, C. Madlock-Brown, M.A. Haendel, P.N. Robinson, H. Spratt, S. Visweswaran, J.E. Flack, Y.J. Yoo, D. Gabriel, G.C. Alexander, H.B. Mehta, F. Liu, R.T. Miller, R. Wong, E.L. Hill, L.E. Thorpe, J. Divers
GRAPE for fast and scalable graph processing and random-walk-based embedding
2023 L. Cappelletti, T. Fontana, E. Casiraghi, V. Ravanmehr, T.J. Callahan, C. Cano, M.P. Joachimiak, C.J. Mungall, P.N. Robinson, J. Reese, G. Valentini
NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study
2022 J.T. Reese, B. Coleman, L. Chan, H. Blau, T.J. Callahan, L. Cappelletti, T. Fontana, K.R. Bradwell, N.L. Harris, E. Casiraghi, G. Valentini, G. Karlebach, R. Deer, J.A. Mcmurry, M.A. Haendel, C.G. Chute, E. Pfaff, R. Moffitt, H. Spratt, J.A. Singh, C.J. Mungall, A.E. Williams, P.N. Robinson
Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques
2022 L. Cappelletti, A. Petrini, J. Gliozzo, E. Casiraghi, M. Schubach, M. Kircher, G. Valentini
Cyclooxygenase inhibitor use is associated with increased COVID-19 severity
2021 J. Reese, B. Coleman, L. Chan, T. J Callahan, L. Cappelletti, T. Fontana, K. Rebecca Bradwell, N. L Harris, E. Casiraghi, G. Valentini, G. Karlebach, R. Deer, J. A McMurry, M. A Haendel, C. G Chute, E. Pfaff, R. Moffitt, H. Spratt, J. Singh, C. J Mungall, A. E Williams, P. N Robinson
Semi-automatic Column Type Inference for CSV Table Understanding
2021 S. Bonfitto, L. Cappelletti, F. Trovato, G. Valentini, M. Mesiti
A Web Tool for the Semantic Integration of Heterogeneous and Complex Spreadsheet Tables
2021 S. Bonfitto, L. Cappelletti, E. Casiraghi, P. Perlasca, F. Trovato, G. Valentini, M. Mesiti
Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer
2021 V. Ravanmehr, H. Blau, L. Cappelletti, T. Fontana, L. Carmody, B. Coleman, J. George, J. Reese, M. Joachimiak, G. Bocci, P. Hansen, C. Bult, J. Rueter, E. Casiraghi, G. Valentini, C. Mungall, T.I. Oprea, P.N. Robinson
Bayesian Optimization Improves Tissue-Specific Prediction of Active Regulatory Regions with Deep Neural Networks
2020 L. Cappelletti, A. Petrini, J. Gliozzo, E. Casiraghi, M. Schubach, M. Kircher, G. Valentini
Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments
2020 E. Casiraghi, D. Malchiodi, G. Trucco, M. Frasca, L. Cappelletti, F. Tommaso, A.A. Esposito, E. Avola, A. Jachetti, J. Reese, A. Rizzi, P.N. Robinson, G. Valentini
parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants
2020 A. Petrini, M. Mesiti, M. Schubach, M. Frasca, D. Danis, M. Re, G. Grossi, L. Cappelletti, T. Castrignano, P.N. Robinson, G. Valentini
Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling
2020 L. Cappelletti, T. Fontana, G.W.D. Donato, L.D. Tucci, E. Casiraghi, G. Valentini
Training Neural Networks with Balanced Mini-batch to Improve the Prediction of Pathogenic Genomic Variants in Mendelian Diseases
2019 L. Cappelletti, J. Gliozzo, A. Petrini, G. Valentini
A neural model for the prediction of pathogenic genomic variants in Mendelian diseases
2019 A. Cuzzocrea, L. Cappelletti, G. Valentini