DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computation...

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Veröffentlicht in:	PLoS computational biology 2024-04, Vol.20 (4), p.e1012012-e1012012
Hauptverfasser:	Liu, Hui, Wang, Feng, Yu, Jian, Pan, Yong, Gong, Chaoju, Zhang, Liang, Zhang, Lin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analysis Bioinformatics Biology and Life Sciences Breast cancer Cancer Cancer therapies Care and treatment Cluster analysis Clustering Colorectal cancer Computer and Information Sciences Datasets Decision making Dose-response relationship (Biochemistry) Drug development Drugs Engineering and Technology Factorization Liver cancer Mathematical models Medical research Medicine and Health Sciences Methods Neural networks Oncology, Experimental Physical Sciences Precision medicine Predictions Research and Analysis Methods Signal transduction Sparsity Testing Tumor cell lines
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