Integrative graph regularized matrix factorization for drug-pathway associations analysis. (February 2019) - Dai, Ling-Yun; Zheng, Chun-Hou; Liu, Jin-Xing; Zhu, Rong; Yuan, Sha-Sha; Wang, Juan; Kong, Xiang-Zhen
Graphical abstract: We propose a novel drug-pathway association identification method: Integrative Graph regularized Matrix Factorization (IGMF). It employs graph regularization to encode data geometrical information and prevent possible overfitting in prediction. Furthermore, it achieves parts-based and sparse data representation by imposing L1-norm regularization on the objective function. Empirical studies demonstrate that IGMF has strong advantages in identifying more new drug-pathway associations compared to its peer methods. It further shows a good capability to unveil the intrinsic structures of data. As an effective drug-pathway discovery method, it will inspire new analytics methods in this subfield. Highlights: Integrative Graph regularized Matrix Factorization (IGMF) can make full use of prior knowledge to integrate and analyze gene expression data and drug sensitivity data. The main innovation point is that graph regularization is incorporated into the integrative matrix factorization model to encode data geometrical information and prevent possible overfitting in prediction. Furthermore, it achieves parts-based and sparse data representation by imposing L1-norm regularization on the objective function. We design an optimization method to avoid singular values in matrix decomposition results. Abstract: Pathway-based drug discovery can give full consideration to the efficacy of compounds in the systemic physiological environment. The recently emerged drug-pathway association identification approaches gain popularity due to its potential to decipher the mechanism of action and the targets of compounds. In this study, we propose a novel drug-pathway association identification method: Integrative Graph regularized Matrix Factorization (IGMF). It employs graph regularization to encode data geometrical information and prevent possible overfitting in prediction. Furthermore, it achieves parts-based and sparse data representation by imposing L1 -norm regularization on the objective function. Empirical studies demonstrate that IGMF has strong advantages in identifying more new drug-pathway associations compared to its peer methods. It further shows a good capability to unveil the intrinsic structures of data. As an effective drug-pathway discovery method, it will inspire new analytics methods in this subfield.