Identifying potential association on gene-disease network via dual hypergraph regularized least squares

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.

Genomic Variation Prediction: A Summary From Different Views

“…Because they do not change the coded amino acids, synonymous mutations were once mistakenly thought to have no biological function ( Hong et al, 2020 ; Tang et al, 2020 ; Cheng et al, 2021a ). However, later systematic studies have shown that synonymous mutations are involved in a variety of biological processes and play important roles in the occurrence and development of diseases ( Li et al, 2020b ; Yang et al, 2021a ). Whole genome sequencing using reversible terminator chemistry can generate accurate nucleotide sequences of billions of bases at low cost ( Bentley et al, 2008 ), which greatly improves the data obtained in sequencing projects.…”