Abstract
Large annotated cell line collections have been proven to enable the prediction of drug response in the pre-clinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from the Cancer Cell Line Encyclopedia (CCLE), containing the connections among cell lines and drugs by means of their IC50 values, and we integrated it by linking cell lines to their respective tissue of origin and genomic profile. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.
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Supported by the ERC Advanced Grant 693174 “Data-Driven Genomic Computing” (GeCo).
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Testa, C., Pidò, S., Pinoli, P. (2022). A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_8
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DOI: https://doi.org/10.1007/978-3-031-20837-9_8
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