GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction

• Published in: Interdisciplinary sciences : computational life sciences 2018-09, Vol.10 (3), p.572-582
• Main Authors: Zhang, Yaogong, Liu, Jiahui, Liu, Xiaohu, Hong, Yuxiang, Fan, Xin, Huang, Yalou, Wang, Yuan, Xie, Maoqiang
• Format: Article
• Language: English
• ISSN: 1867-1462
• DOI: 10.1007/s12539-018-0296-1

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC[Formula: see text]NMF). Specifically, first we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchical phenotypic data as weighted graph constraint for a better phenotype understanding. Second, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us with the intuition that genes in a group probably result in similar phenotypes. The model not only allows us to achieve a high-grade prediction performance, but also helps us to learn interpretable representation of genes and phenotypes simultaneously to facilitate future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC[Formula: see text]NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-arts methods.