Mk-Nmf: A Novel Multiple Kernel-Based Non-Negative Matrix Factorization Model to Mini Synergistic Drug Combinations in Cell Lines

作     者：Li, Tianyi Han, Huirui Chen, Jiaqi Feng, Dehua Chen, Zhengxin Wang, Xuefeng Liu, Xinying Zhang, Ruijie Wang, Qibin Yu, Lei Li, Xia Li, Bing Wang, Limei Li, Jin 

作者机构：College of Biomedical Information and Engineering Kidney Disease Research Institute Second Affiliated Hospital Hainan Engineering Research Center for Health Big Data Key Laboratory of Tropical Translational Medicine Ministry of Education Hainan Medical University Hainan Haikou571199 China 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Non negative matrix factorization 

摘      要：Drug synergism may occur when two or more drugs are used in combination. Synergistic drug pairs can enhance efficacy and reduce drug dosage and side effects. It is meaningful to use computational methods to mine specific synergistic drug combinations for clinical treatment. We proposed a multiple kernel-based non-negative matrix factorization, MK-NMF, specifically for mining specific synergistic drug pairs in cell lines. In this method, we treated the features of drug pair space and cell line space in the form of two kernel matrices. We incorporated feature kernel matrices into the matrix factorization process. MK-NMF achieved an area under the curve (AUC) of 0.884 and an area under the precision versus recall curve (AUPR) of 0.537 on the NCI ALMANAC dataset. Both measures were more than a 5% improvement over the previous matrix factorization model. MK-NMF had good robustness with the missing input data. Its performance was stable when the amount of input matrix data was not less than 40%. Literature and experimental verification confirmed some of our predictions. MK-NMF could assist medical professionals in rapidly screening synergistic drug combinations against specific cancer cell lines. © 2024, The Authors. All rights reserved.