HNGRNMF: Heterogeneous Network-based Graph Regularized Nonnegative Matrix Factorization for predicting events of microbe-disease associations

• Published in: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 803 - 807
• Main Authors: Zhang, Wen, Lu, Xiaoting, Yang, Weitai, Huang, Feng, Wang, Binlu, Wang, Alan, Zhao, Qi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2018
• Subjects: Diseases; heterogeneous network; Heterogeneous networks; Kernel; Laplace equations; Matrix decomposition; microbe-disease associations; Microorganisms; nonnegative matrix factorization; Semantics
• DOI: 10.1109/BIBM.2018.8621085

Identifying the microbe-disease associations provides a great insight into the mechanism that microbes cause diseases at the molecular level. Existing computational methods utilized known microbe-disease associations, microbe features and disease features to predict novel associations. However, there exist different events related to microbe-disease associations, for example, the species abundance of a microbe in bodies will increase or decrease when patients have a disease. In this paper, we propose a heterogeneous network-based graph regularized nonnegative matrix factorization (HNGRNMF) to predict microbe-disease associations as well as the events. HNGRNMF constructs a heterogeneous network based on observed microbe-disease association-related events: increase/decrease on species abundance of microbes, and extracts two event matrices from the heterogeneous network, and then decomposes matrices into the common low-rank disease space and two different low-rank microbe spaces under the frame of nonnegative matrix factorization. In addition, microbe-microbe similarities and disease-disease similarities are integrated, respectively, and incorporated into HNGRNMF. The experiment results demonstrate that HNGRNMF accurately predicts microbe-disease associations and their events when evaluated by leave-one-out cross validation and 5-fold cross validation.