Messa, L.; Testa, C.; Carelli, S.; Rey, F.; Jacchetti, E.; Cereda, C.; Raimondi, M.T.; Ceri, S.; Pinoli, P.
Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection. Int. J. Mol. Sci. 2024, 25, 9576.
https://doi.org/10.3390/ijms25179576
AMA Style
Messa L, Testa C, Carelli S, Rey F, Jacchetti E, Cereda C, Raimondi MT, Ceri S, Pinoli P.
Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection. International Journal of Molecular Sciences. 2024; 25(17):9576.
https://doi.org/10.3390/ijms25179576
Chicago/Turabian Style
Messa, Letizia, Carolina Testa, Stephana Carelli, Federica Rey, Emanuela Jacchetti, Cristina Cereda, Manuela Teresa Raimondi, Stefano Ceri, and Pietro Pinoli.
2024. "Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection" International Journal of Molecular Sciences 25, no. 17: 9576.
https://doi.org/10.3390/ijms25179576
APA Style
Messa, L., Testa, C., Carelli, S., Rey, F., Jacchetti, E., Cereda, C., Raimondi, M. T., Ceri, S., & Pinoli, P.
(2024). Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection. International Journal of Molecular Sciences, 25(17), 9576.
https://doi.org/10.3390/ijms25179576
