Sfoglia per Autore
Human leukocyte antigen polymorphisms in Italian primary biliary cirrhosis: a multicenter study of 664 patients and 1992 healthy controls
2008-01-01 Italian PBC Genetic Study, Group; Podda, M; Gershwin, Me; Seldin, Mf; Qi, L; Muratori, L; Fabris, L; Marzioni, M; Rosina, F; Almasio, P; Alvaro, D; Floreani, A; Frison, S; Poli, F; Selmi, C; Invernizzi PAndreoletti, M; Andriulli, A; Baldini, V; Battezzati, Pm; Benedetti, A; Bernuzzi, F; Bianchi, Fb; Bianchi, I; Bignotto, M; Bragazzi, Mc; Brunetto, M; Caimi, M; Caliari, L; Caporaso, N; Casella, G; Casiraghi, A; Colli, A; Colombo, M; Conte, D; Croce, L; Crosignani, A; Dottorini, L; Ferrari, C; Fraquelli, M; Frati, Ce; Galli, A; Lleo, A; Mancino, Mg; Mandelli, G; Marra, F; Montanari, R; Monti, V; Morini, L; Morisco, F; Niro, G; Palasciano, G; Calmieri, Vo; Pasini, S; Picciotto, A; Portincasa, P; Pozzoli, V; Spinzi, Cinzia Giacinta; Strazzabosco, M; Tiribelli, C; Toniutto, P; Zerminai, P; Zuin, M.
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor
2018-01-01 Podda, Marco; Bacciu, Davide; Micheli, Alessio; Bellù, Roberto; Placidi, Giulia; Gagliardi, Luigi
Graph generation by sequential edge prediction
2019-01-01 Bacciu, D.; Micheli, A.; Podda, M.
Preliminary Results on Predicting Robustness ofBiochemical Pathways through MachineLearning on Graphs
2019-01-01 Bove, P.; Micheli, A.; Milazzo, P.; Podda, M.
A gentle introduction to deep learning for graphs
2020-01-01 Bacciu, D.; Errica, F.; Micheli, A.; Podda, M.
Prediction of dynamical properties of biochemical pathways with graph neural networks
2020-01-01 Bove, P.; Micheli, A.; Milazzo, P.; Podda, M.
A Deep Generative Model for Fragment-Based Molecule Generation
2020-01-01 Podda, M; Bacciu, D; Micheli, A
Edge-based sequential graph generation with recurrent neural networks
2020-01-01 Bacciu, D.; Micheli, A.; Podda, M.
Biochemical pathway robustness prediction with graph neural networks
2020-01-01 Podda, M.; Bacciu, D.; Micheli, A.; Milazzo, P.
A Fair Comparison of Graph Neural Networks for Graph Classification
2020-01-01 Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio
Graphgen-redux: A Fast and Lightweight Recurrent Model for labeled Graph Generation
2021-01-01 Bacciu, D.; Podda, M.
Classification of Biochemical Pathway Robustness with Neural Networks for Graphs
2021-01-01 Podda, M.; Bove, P.; Micheli, A.; Milazzo, P.
In silico modeling of biochemical pathways
2021-01-01 Milazzo, Paolo; Gori, Roberta; Micheli, Alessio; Nasti, Lucia; Podda, Marco
Deep Learning in Cheminformatics
2022-01-01 Micheli, Alessio; Podda, Marco
Deep Graph Networks for Drug Repurposing with Multi-Protein Targets
2023-01-01 Bacciu, Davide; Errica, Federico; Gravina, Alessio; Madeddu, Lorenzo; Podda, Marco; Stilo, Giovanni
Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs
2023-01-01 Fontanesi, Michele; Micheli, Alessio; Milazzo, Paolo; Podda, Marco
Graph Representation Learning
2023-01-01 Bacciu, Davide; Errica, Federico; Micheli, Alessio; Navarin, Nicolò; Pasa, Luca; Podda, Marco; Zambon, Daniele
Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning
2024-01-01 Podda, Marco; Bonechi, Simone; Palladino, Andrea; Scaramuzzino, Mattia; Brozzi, Alessandro; Roma, Guglielmo; Muzzi, Alessandro; Priami, Corrado; Sirbu, Alina; Bodini, Margherita
| Titolo | Data di pubblicazione | Autore(i) | File |
|---|---|---|---|
| Human leukocyte antigen polymorphisms in Italian primary biliary cirrhosis: a multicenter study of 664 patients and 1992 healthy controls | 1-gen-2008 | Italian PBC Genetic Study, Group; Podda, M; Gershwin, Me; Seldin, Mf; Qi, L; Muratori, L; Fabris, L; Marzioni, M; Rosina, F; Almasio, P; Alvaro, D; Floreani, A; Frison, S; Poli, F; Selmi, C; Invernizzi PAndreoletti, M; Andriulli, A; Baldini, V; Battezzati, Pm; Benedetti, A; Bernuzzi, F; Bianchi, Fb; Bianchi, I; Bignotto, M; Bragazzi, Mc; Brunetto, M; Caimi, M; Caliari, L; Caporaso, N; Casella, G; Casiraghi, A; Colli, A; Colombo, M; Conte, D; Croce, L; Crosignani, A; Dottorini, L; Ferrari, C; Fraquelli, M; Frati, Ce; Galli, A; Lleo, A; Mancino, Mg; Mandelli, G; Marra, F; Montanari, R; Monti, V; Morini, L; Morisco, F; Niro, G; Palasciano, G; Calmieri, Vo; Pasini, S; Picciotto, A; Portincasa, P; Pozzoli, V; Spinzi, Cinzia Giacinta; Strazzabosco, M; Tiribelli, C; Toniutto, P; Zerminai, P; Zuin, M. | |
| A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor | 1-gen-2018 | Podda, Marco; Bacciu, Davide; Micheli, Alessio; Bellù, Roberto; Placidi, Giulia; Gagliardi, Luigi | |
| Graph generation by sequential edge prediction | 1-gen-2019 | Bacciu, D.; Micheli, A.; Podda, M. | |
| Preliminary Results on Predicting Robustness ofBiochemical Pathways through MachineLearning on Graphs | 1-gen-2019 | Bove, P.; Micheli, A.; Milazzo, P.; Podda, M. | |
| A gentle introduction to deep learning for graphs | 1-gen-2020 | Bacciu, D.; Errica, F.; Micheli, A.; Podda, M. | |
| Prediction of dynamical properties of biochemical pathways with graph neural networks | 1-gen-2020 | Bove, P.; Micheli, A.; Milazzo, P.; Podda, M. | |
| A Deep Generative Model for Fragment-Based Molecule Generation | 1-gen-2020 | Podda, M; Bacciu, D; Micheli, A | |
| Edge-based sequential graph generation with recurrent neural networks | 1-gen-2020 | Bacciu, D.; Micheli, A.; Podda, M. | |
| Biochemical pathway robustness prediction with graph neural networks | 1-gen-2020 | Podda, M.; Bacciu, D.; Micheli, A.; Milazzo, P. | |
| A Fair Comparison of Graph Neural Networks for Graph Classification | 1-gen-2020 | Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio | |
| Graphgen-redux: A Fast and Lightweight Recurrent Model for labeled Graph Generation | 1-gen-2021 | Bacciu, D.; Podda, M. | |
| Classification of Biochemical Pathway Robustness with Neural Networks for Graphs | 1-gen-2021 | Podda, M.; Bove, P.; Micheli, A.; Milazzo, P. | |
| In silico modeling of biochemical pathways | 1-gen-2021 | Milazzo, Paolo; Gori, Roberta; Micheli, Alessio; Nasti, Lucia; Podda, Marco | |
| Deep Learning in Cheminformatics | 1-gen-2022 | Micheli, Alessio; Podda, Marco | |
| Deep Graph Networks for Drug Repurposing with Multi-Protein Targets | 1-gen-2023 | Bacciu, Davide; Errica, Federico; Gravina, Alessio; Madeddu, Lorenzo; Podda, Marco; Stilo, Giovanni | |
| Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs | 1-gen-2023 | Fontanesi, Michele; Micheli, Alessio; Milazzo, Paolo; Podda, Marco | |
| Graph Representation Learning | 1-gen-2023 | Bacciu, Davide; Errica, Federico; Micheli, Alessio; Navarin, Nicolò; Pasa, Luca; Podda, Marco; Zambon, Daniele | |
| Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning | 1-gen-2024 | Podda, Marco; Bonechi, Simone; Palladino, Andrea; Scaramuzzino, Mattia; Brozzi, Alessandro; Roma, Guglielmo; Muzzi, Alessandro; Priami, Corrado; Sirbu, Alina; Bodini, Margherita |
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