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 co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-021-07864-z |
| _version_ | 1818363307762909184 |
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| author | Hongpeng Yang Yijie Ding Jijun Tang Fei Guo |
| author_facet | Hongpeng Yang Yijie Ding Jijun Tang Fei Guo |
| author_sort | Hongpeng Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T21:46:24Z |
| format | Article |
| id | doaj.art-566a4993c5b940b4adf447b1edfdd4ee |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-13T21:46:24Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-566a4993c5b940b4adf447b1edfdd4ee2022-12-21T23:30:24ZengBMCBMC Genomics1471-21642021-08-0122111610.1186/s12864-021-07864-zIdentifying potential association on gene-disease network via dual hypergraph regularized least squaresHongpeng Yang0Yijie Ding1Jijun Tang2Fei Guo3School of Computer Science and Technology, College of Intelligence and Computing, Tianjin UniversityYangtze Delta Region Institute, University of Electronic Science and Technology of ChinaShenzhen Institute of Advanced Technology, Chinese Academy of SciencesSchool of Computer Science and Engineering, Central South UniversityAbstract 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.https://doi.org/10.1186/s12864-021-07864-zGene-disease association networkHypergraph learningDual Laplacian regularized least squaresBipartite networkMultiple kernel learning |
| spellingShingle | Hongpeng Yang Yijie Ding Jijun Tang Fei Guo Identifying potential association on gene-disease network via dual hypergraph regularized least squares BMC Genomics Gene-disease association network Hypergraph learning Dual Laplacian regularized least squares Bipartite network Multiple kernel learning |
| title | Identifying potential association on gene-disease network via dual hypergraph regularized least squares |
| title_full | Identifying potential association on gene-disease network via dual hypergraph regularized least squares |
| title_fullStr | Identifying potential association on gene-disease network via dual hypergraph regularized least squares |
| title_full_unstemmed | Identifying potential association on gene-disease network via dual hypergraph regularized least squares |
| title_short | Identifying potential association on gene-disease network via dual hypergraph regularized least squares |
| title_sort | identifying potential association on gene disease network via dual hypergraph regularized least squares |
| topic | Gene-disease association network Hypergraph learning Dual Laplacian regularized least squares Bipartite network Multiple kernel learning |
| url | https://doi.org/10.1186/s12864-021-07864-z |
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