Hongpeng Yang1, Jijun Tang2, Yijie Ding3, Fei Guo4. 1. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China. 2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 3. Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China. wuxi_dyj@163.com. 4. School of Computer Science and Engineering, Central South University, Changsha, China. guofeieileen@163.com.
Abstract
BACKGROUND: Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. RESULTS: In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. CONCLUSION: Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.
BACKGROUND: Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. RESULTS: In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. CONCLUSION: Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.
Authors: U Martin Singh-Blom; Nagarajan Natarajan; Ambuj Tewari; John O Woods; Inderjit S Dhillon; Edward M Marcotte Journal: PLoS One Date: 2013-05-01 Impact factor: 3.240
Authors: Rahul C Deo; Gabriel Musso; Murat Tasan; Paul Tang; Annie Poon; Christiana Yuan; Janine F Felix; Ramachandran S Vasan; Rameen Beroukhim; Teresa De Marco; Pui-Yan Kwok; Calum A MacRae; Frederick P Roth Journal: Genome Biol Date: 2014-12-03 Impact factor: 13.583