Sfoglia per Titolo
M. D. Anderson symptom inventory head neck (MDASI-HN) questionnaire: Italian language psychometric validation in head and neck cancer patients treated with radiotherapy ± systemic therapy – A study of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)
2021-01-01 Vigano, A.; De Felice, F.; Iacovelli, N. A.; Alterio, D.; Facchinetti, N.; Oneta, O.; Bacigalupo, A.; Tornari, E.; Ursino, S.; Paiar, F.; Caspiani, O.; Di Rito, A.; Musio, D.; Bossi, P.; Steca, P.; Jereczek-Fossa, B. A.; Greco, A.; Orlandi, E.
M. Nava, G, Catanuto, G. Querci della Rovere's letter to the editor, Ref: Wise pattern mastectomy with immediate breast reconstruction by P. Prathap and R.N. L. Harland. The breast 13, 502-5, 2004-authors reply
2005-01-01 Catanuto, G
M2 macrophages phagocytose rituximab-opsonized leukemic targets more efficiently than m1 cells in vitro
2009-01-01 Leidi, M.; Gotti, E.; Bologna, L.; Miranda, E.; Rimoldi, M.; Sica, A.; Roncalli, M.; Palumbo, G. A.; Introna, M.; Golay, J.
M3C: Monte Carlo reference-based consensus clustering
2020-01-01 John, Christopher R.; Watson, David; Russ, Dominic; Goldmann, Katriona; Ehrenstein, Michael; Pitzalis, Costantino; Lewis, Myles; Barnes, Michael
m6A-driven SF3B1 translation control steers splicing to direct genome integrity and leukemogenesis
2023-01-01 Cieśla, Maciej; Ngoc, Phuong Cao Thi; Muthukumar, Sowndarya; Todisco, Gabriele; Madej, Magdalena; Fritz, Helena; Dimitriou, Marios; Incarnato, Danny; Hellström-Lindberg, Eva; Bellodi, Cristian
Mab21, the mouse homolog of a C. elegans cell-fate specification gene, participates in cerebellar, midbrain and eye development.
1998-01-01 Fesce, RICCARDO GIUSEPPE
Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain
2023-01-01 Caldo, Davide; Bologna, Silvia; Conte, Luana; Amin, Muhammad Saad; Anselma, Luca; Basile, Valerio; Hossain, Md. Murad; Mazzei, Alessandro; Heritier, Paolo; Ferracini, Riccardo; Kon, Elizaveta; De Nunzio, Giorgio
Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans
2024-01-01 Lambri, Nicola; Dei, Damiano; Goretti, Giulia; Crespi, Leonardo; Brioso, Ricardo Coimbra; Pelizzoli, Marco; Parabicoli, Sara; Bresolin, Andrea; Gallo, Pasqualina; La Fauci, Francesco; Lobefalo, Francesca; Paganini, Lucia; Reggiori, Giacomo; Loiacono, Daniele; Franzese, Ciro; Tomatis, Stefano; Scorsetti, Marta; Mancosu, Pietro
Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies
2021-01-01 Infante, Teresa; Francone, Marco; De Rimini, Maria L; Cavaliere, Carlo; Canonico, Raffaele; Catalano, Carlo; Napoli, Claudio
Machine Learning and Syncope Management in the ED: The Future Is Coming
2021-01-01 Dipaola, Franca; Shiffer, Dana; Gatti, Mauro; Menè, Roberto; Solbiati, Monica; Furlan, Raffaello
Machine Learning Application Identifies Germline Markers of Hypertension in Patients With Ovarian Cancer Treated With Carboplatin, Taxane, and Bevacizumab
2023-01-01 Polano, Maurizio; Bedon, Luca; Dal Bo, Michele; Sorio, Roberto; Bartoletti, Michele; De Mattia, Elena; Cecchin, Erika; Pisano, Carmela; Lorusso, Domenica; Lissoni, Andrea Alberto; De Censi, Andrea; Cecere, Sabrina Chiara; Scollo, Paolo; Marchini, Sergio; Arenare, Laura; De Giorgi, Ugo; Califano, Daniela; Biagioli, Elena; Chiodini, Paolo; Perrone, Francesco; Pignata, Sandro; Toffoli, Giuseppe
Machine Learning Applications in the Study of Parkinson’s Disease: A Systematic Review
2023-01-01 Martorell-Marugán, Jordi; Chierici, Marco; Bandres-Ciga, Sara; Jurman, Giuseppe; Carmona-Sáez, Pedro
Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review
2021-01-01 Abdelkader, W; Navarro, T; Parrish, R; Cotoi, C; Germini, F; Iorio, A; Haynes, Rb; Lokker, C
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
2020-01-01 Chicco, Davide; Jurman, Giuseppe
Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
2023-01-01 Greco, Massimiliano; Caruso, Pier Francesco; Spano, Sofia; Citterio, Gianluigi; Desai, Antonio; Molteni, Alberto; Aceto, Romina; Costantini, Elena; Voza, Antonio; Cecconi, Maurizio
Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges
2021-01-01 Weikert, Thomas; Francone, Marco; Abbara, Suhny; Baessler, Bettina; Choi, Byoung Wook; Gutberlet, Matthias; Hecht, Elizabeth M; Loewe, Christian; Mousseaux, Elie; Natale, Luigi; Nikolaou, Konstantin; Ordovas, Karen G; Peebles, Charles; Prieto, Claudia; Salgado, Rodrigo; Velthuis, Birgitta; Vliegenthart, Rozemarijn; Bremerich, Jens; Leiner, Tim
Machine learning methods for predictive proteomics
2008-01-01 A., Barla; Jurman, Giuseppe; Riccadonna, Samantha; Chierici, Marco; Merler, Stefano; Furlanello, Cesare
Machine learning methods for predictive proteomics
2008-01-01 Barla, A; Jurman, G; Riccadonna, S; Merler, S; Chierici, M; Furlanello, C
Machine learning models for predicting endocrine disruption potential of environmental chemicals
2019-01-01 Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare
Machine learning models for predicting endocrine disruption potential of environmental chemicals
2019-01-01 Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare
Titolo | Data di pubblicazione | Autore(i) | File |
---|---|---|---|
M. D. Anderson symptom inventory head neck (MDASI-HN) questionnaire: Italian language psychometric validation in head and neck cancer patients treated with radiotherapy ± systemic therapy – A study of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) | 1-gen-2021 | Vigano, A.; De Felice, F.; Iacovelli, N. A.; Alterio, D.; Facchinetti, N.; Oneta, O.; Bacigalupo, A.; Tornari, E.; Ursino, S.; Paiar, F.; Caspiani, O.; Di Rito, A.; Musio, D.; Bossi, P.; Steca, P.; Jereczek-Fossa, B. A.; Greco, A.; Orlandi, E. | |
M. Nava, G, Catanuto, G. Querci della Rovere's letter to the editor, Ref: Wise pattern mastectomy with immediate breast reconstruction by P. Prathap and R.N. L. Harland. The breast 13, 502-5, 2004-authors reply | 1-gen-2005 | Catanuto, G | |
M2 macrophages phagocytose rituximab-opsonized leukemic targets more efficiently than m1 cells in vitro | 1-gen-2009 | Leidi, M.; Gotti, E.; Bologna, L.; Miranda, E.; Rimoldi, M.; Sica, A.; Roncalli, M.; Palumbo, G. A.; Introna, M.; Golay, J. | |
M3C: Monte Carlo reference-based consensus clustering | 1-gen-2020 | John, Christopher R.; Watson, David; Russ, Dominic; Goldmann, Katriona; Ehrenstein, Michael; Pitzalis, Costantino; Lewis, Myles; Barnes, Michael | |
m6A-driven SF3B1 translation control steers splicing to direct genome integrity and leukemogenesis | 1-gen-2023 | Cieśla, Maciej; Ngoc, Phuong Cao Thi; Muthukumar, Sowndarya; Todisco, Gabriele; Madej, Magdalena; Fritz, Helena; Dimitriou, Marios; Incarnato, Danny; Hellström-Lindberg, Eva; Bellodi, Cristian | |
Mab21, the mouse homolog of a C. elegans cell-fate specification gene, participates in cerebellar, midbrain and eye development. | 1-gen-1998 | Fesce, RICCARDO GIUSEPPE | |
Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain | 1-gen-2023 | Caldo, Davide; Bologna, Silvia; Conte, Luana; Amin, Muhammad Saad; Anselma, Luca; Basile, Valerio; Hossain, Md. Murad; Mazzei, Alessandro; Heritier, Paolo; Ferracini, Riccardo; Kon, Elizaveta; De Nunzio, Giorgio | |
Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans | 1-gen-2024 | Lambri, Nicola; Dei, Damiano; Goretti, Giulia; Crespi, Leonardo; Brioso, Ricardo Coimbra; Pelizzoli, Marco; Parabicoli, Sara; Bresolin, Andrea; Gallo, Pasqualina; La Fauci, Francesco; Lobefalo, Francesca; Paganini, Lucia; Reggiori, Giacomo; Loiacono, Daniele; Franzese, Ciro; Tomatis, Stefano; Scorsetti, Marta; Mancosu, Pietro | |
Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies | 1-gen-2021 | Infante, Teresa; Francone, Marco; De Rimini, Maria L; Cavaliere, Carlo; Canonico, Raffaele; Catalano, Carlo; Napoli, Claudio | |
Machine Learning and Syncope Management in the ED: The Future Is Coming | 1-gen-2021 | Dipaola, Franca; Shiffer, Dana; Gatti, Mauro; Menè, Roberto; Solbiati, Monica; Furlan, Raffaello | |
Machine Learning Application Identifies Germline Markers of Hypertension in Patients With Ovarian Cancer Treated With Carboplatin, Taxane, and Bevacizumab | 1-gen-2023 | Polano, Maurizio; Bedon, Luca; Dal Bo, Michele; Sorio, Roberto; Bartoletti, Michele; De Mattia, Elena; Cecchin, Erika; Pisano, Carmela; Lorusso, Domenica; Lissoni, Andrea Alberto; De Censi, Andrea; Cecere, Sabrina Chiara; Scollo, Paolo; Marchini, Sergio; Arenare, Laura; De Giorgi, Ugo; Califano, Daniela; Biagioli, Elena; Chiodini, Paolo; Perrone, Francesco; Pignata, Sandro; Toffoli, Giuseppe | |
Machine Learning Applications in the Study of Parkinson’s Disease: A Systematic Review | 1-gen-2023 | Martorell-Marugán, Jordi; Chierici, Marco; Bandres-Ciga, Sara; Jurman, Giuseppe; Carmona-Sáez, Pedro | |
Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review | 1-gen-2021 | Abdelkader, W; Navarro, T; Parrish, R; Cotoi, C; Germini, F; Iorio, A; Haynes, Rb; Lokker, C | |
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone | 1-gen-2020 | Chicco, Davide; Jurman, Giuseppe | |
Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department | 1-gen-2023 | Greco, Massimiliano; Caruso, Pier Francesco; Spano, Sofia; Citterio, Gianluigi; Desai, Antonio; Molteni, Alberto; Aceto, Romina; Costantini, Elena; Voza, Antonio; Cecconi, Maurizio | |
Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges | 1-gen-2021 | Weikert, Thomas; Francone, Marco; Abbara, Suhny; Baessler, Bettina; Choi, Byoung Wook; Gutberlet, Matthias; Hecht, Elizabeth M; Loewe, Christian; Mousseaux, Elie; Natale, Luigi; Nikolaou, Konstantin; Ordovas, Karen G; Peebles, Charles; Prieto, Claudia; Salgado, Rodrigo; Velthuis, Birgitta; Vliegenthart, Rozemarijn; Bremerich, Jens; Leiner, Tim | |
Machine learning methods for predictive proteomics | 1-gen-2008 | A., Barla; Jurman, Giuseppe; Riccadonna, Samantha; Chierici, Marco; Merler, Stefano; Furlanello, Cesare | |
Machine learning methods for predictive proteomics | 1-gen-2008 | Barla, A; Jurman, G; Riccadonna, S; Merler, S; Chierici, M; Furlanello, C | |
Machine learning models for predicting endocrine disruption potential of environmental chemicals | 1-gen-2019 | Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare | |
Machine learning models for predicting endocrine disruption potential of environmental chemicals | 1-gen-2019 | Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare |
Legenda icone
- file ad accesso aperto
- file disponibili sulla rete interna
- file disponibili agli utenti autorizzati
- file disponibili solo agli amministratori
- file sotto embargo
- nessun file disponibile