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Yao X, Ouyang S, Lian Y, Peng Q, Zhou X, Huang F, Hu X, Shi F, Xia J. PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies. Genome Med 2024; 16:56. [PMID: 38627848 PMCID: PMC11020195 DOI: 10.1186/s13073-024-01330-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.
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Affiliation(s)
- Xinzhi Yao
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Sizhuo Ouyang
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Yulong Lian
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Qianqian Peng
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Xionghui Zhou
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feier Huang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xuehai Hu
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feng Shi
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Jingbo Xia
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
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Wang Z, Gu Y, Zheng S, Yang L, Li J. MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction. Comput Biol Med 2023; 155:106642. [PMID: 36805231 DOI: 10.1016/j.compbiomed.2023.106642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/15/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associations in recent years. However, most of them ignored the combination of abundant genetic, therapeutic information, and gene-disease network topology. To this end, we re-organized the current gene-disease association benchmark dataset by extracting the newest gene-disease associations from the OMIM database. Then, we developed a multi-graph representation learning-based ensemble model, named MGREL to predict gene-disease associations. MGREL integrated two feature generation channels to extract gene and disease features, including a knowledge extraction channel which learned high-order representations from genetic and therapeutic information, and a graph learning channel which acquired network topological representations through multiple advanced graph representation learning methods. Then, an ensemble learning method with 5 machine learning models was used as the classifier to predict the gene-disease association. Comprehensive experiments have demonstrated the significant performance achieved by MGREL compared to 5 state-of-the-art methods. For the major measurements (AUC = 0.925, AUPR = 0.935), the relative improvements of MGREL compared to the suboptimal methods are 3.24%, and 2.75%, respectively. MGREL also achieved impressive improvements in the challenging tasks of predicting potential associations for unknown genes/diseases. In addition, case studies implied potential applications for MGREL in the discovery of potential therapeutic targets.
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Affiliation(s)
- Ziyang Wang
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Yaowen Gu
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Si Zheng
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China; Institute for Artificial Intelligence, Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China
| | - Lin Yang
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Jiao Li
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China.
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Ouyang S, Wang Y, Zhou K, Xia J. LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19. Genomics Inform 2021; 19:e23. [PMID: 34638170 PMCID: PMC8510875 DOI: 10.5808/gi.21013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 12/20/2022] Open
Abstract
Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.
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Affiliation(s)
- Sizhuo Ouyang
- Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, 430070 Wuhan, China
| | - Yuxing Wang
- Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, 430070 Wuhan, China
| | - Kaiyin Zhou
- Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, 430070 Wuhan, China
| | - Jingbo Xia
- Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, 430070 Wuhan, China
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