Sfoglia per Autore
Transcriptomic analysis identifies organ-specific metastasis genes and pathways across different primary sites
2021-01-01 Zhang, L.; Fan, M.; Napolitano, F.; Gao, X.; Xu, Y.; Li, L.
MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data
2021-01-01 Albaradei, S.; Napolitano, F.; Thafar, M. A.; Gojobori, T.; Essack, M.; Gao, X.
Automatic identification of small molecules that promote cell conversion and reprogramming
2021-01-01 Napolitano, F.; Rapakoulia, T.; Annunziata, P.; Hasegawa, A.; Cardon, M.; Napolitano, S.; Vaccaro, L.; Iuliano, A.; Wanderlingh, L. G.; Kasukawa, T.; Medina, D. L.; Cacchiarelli, D.; Gao, X.; di Bernardo, D.; Arner, E.
Fat induces glucose metabolism in nontransformed liver cells and promotes liver tumorigenesis
2021-01-01 Broadfield, L. A.; Duarte, J. A. G.; Schmieder, R.; Broekaert, D.; Veys, K.; Planque, M.; Vriens, K.; Karasawa, Y.; Napolitano, F.; Fujita, S.; Fujii, M.; Eto, M.; Holvoet, B.; Vangoitsenhoven, R.; Fernandez-Garcia, J.; Van Elsen, J.; Dehairs, J.; Zeng, J.; Dooley, J.; Rubio, R. A.; Van Pelt, J.; Grunewald, T. G. P.; Liston, A.; Mathieu, C.; Deroose, C. M.; Swinnen, J. V.; Lambrechts, D.; Di Bernardo, D.; Kuroda, S.; De Bock, K.; Fendt, S. -M.
Special issue on computational biology and bioinformatic applications to the COVID-19 pandemic
2022-01-01 Napolitano, F; Gao, X
Impact of computational approaches in the fight against COVID-19: An AI guided review of 17 000 studies
2022-01-01 Napolitano, F.; Xu, X.; Gao, X.
| Titolo | Data di pubblicazione | Autore(i) | File |
|---|---|---|---|
| Transcriptomic analysis identifies organ-specific metastasis genes and pathways across different primary sites | 1-gen-2021 | Zhang, L.; Fan, M.; Napolitano, F.; Gao, X.; Xu, Y.; Li, L. | |
| MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data | 1-gen-2021 | Albaradei, S.; Napolitano, F.; Thafar, M. A.; Gojobori, T.; Essack, M.; Gao, X. | |
| Automatic identification of small molecules that promote cell conversion and reprogramming | 1-gen-2021 | Napolitano, F.; Rapakoulia, T.; Annunziata, P.; Hasegawa, A.; Cardon, M.; Napolitano, S.; Vaccaro, L.; Iuliano, A.; Wanderlingh, L. G.; Kasukawa, T.; Medina, D. L.; Cacchiarelli, D.; Gao, X.; di Bernardo, D.; Arner, E. | |
| Fat induces glucose metabolism in nontransformed liver cells and promotes liver tumorigenesis | 1-gen-2021 | Broadfield, L. A.; Duarte, J. A. G.; Schmieder, R.; Broekaert, D.; Veys, K.; Planque, M.; Vriens, K.; Karasawa, Y.; Napolitano, F.; Fujita, S.; Fujii, M.; Eto, M.; Holvoet, B.; Vangoitsenhoven, R.; Fernandez-Garcia, J.; Van Elsen, J.; Dehairs, J.; Zeng, J.; Dooley, J.; Rubio, R. A.; Van Pelt, J.; Grunewald, T. G. P.; Liston, A.; Mathieu, C.; Deroose, C. M.; Swinnen, J. V.; Lambrechts, D.; Di Bernardo, D.; Kuroda, S.; De Bock, K.; Fendt, S. -M. | |
| Special issue on computational biology and bioinformatic applications to the COVID-19 pandemic | 1-gen-2022 | Napolitano, F; Gao, X | |
| Impact of computational approaches in the fight against COVID-19: An AI guided review of 17 000 studies | 1-gen-2022 | Napolitano, F.; Xu, X.; Gao, X. |
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