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Technology","award":["2016B090918122"],"award-info":[{"award-number":["2016B090918122"]}]},{"name":"The Funds of Peng Cheng Lab"},{"DOI":"10.13039\/501100011220","name":"State Key Laboratory of Chemo\/Biosensing and Chemometrics","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011220","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approaches to predict relationships among entities. However, current researches slightly explored BioHNs-based self-supervised representation learning methods, and are hard to simultaneously capturing local- and global-level association information among entities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose a BioHN-based self-supervised representation learning approach for entity relationship predictions, termed BioERP. A self-supervised meta path detection mechanism is proposed to train a deep Transformer encoder model that can capture the global structure and semantic feature in BioHNs. Meanwhile, a biomedical entity mask learning strategy is designed to reflect local associations of vertices. Finally, the representations from different task models are concatenated to generate two-level representation vectors for predicting relationships among entities. The results on eight datasets show BioERP outperforms 30 state-of-the-art methods. In particular, BioERP reveals great performance with results close to 1 in terms of AUC and AUPR on the drug\u2013target interaction predictions. In summary, BioERP is a promising bio-entity relationship prediction approach.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code and data can be downloaded from https:\/\/github.com\/pengsl-lab\/BioERP.git.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab565","type":"journal-article","created":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T19:25:54Z","timestamp":1627586754000},"page":"4793-4800","source":"Crossref","is-referenced-by-count":22,"title":["BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5868-5044","authenticated-orcid":false,"given":"Xiaoqi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaning","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, National University of Defense Technology , Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences , Beijing 100850, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoliang","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"},{"name":"School of Computer Science, National University of Defense Technology , Changsha 410073, China"},{"name":"Peng Cheng Laboratory, Vanke Cloud City Phase I Building , Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"2023051607130019300_btab565-B1","first-page":"37","author":"Ahmed","year":"2013"},{"key":"2023051607130019300_btab565-B2","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1093\/bioinformatics\/btt307","article-title":"Drug\u2013target interaction prediction through domain-tuned network-based inference","volume":"29","author":"Alaimo","year":"2013","journal-title":"Bioinformatics"},{"key":"2023051607130019300_btab565-B3","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1093\/bioinformatics\/btx275","article-title":"Neuro-symbolic representation learning on biological knowledge graphs","volume":"33","author":"Alshahrani","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051607130019300_btab565-B4","doi-asserted-by":"crossref","first-page":"i901","DOI":"10.1093\/bioinformatics\/bty559","article-title":"Semantic disease gene embeddings (smudge): phenotype-based disease gene prioritization without phenotypes","volume":"34","author":"Alshahrani","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607130019300_btab565-B5","doi-asserted-by":"crossref","first-page":"baw103","DOI":"10.1093\/database\/baw103","article-title":"HPIDB 2.0: a curated database for host\u2013pathogen interactions","volume":"2016","author":"Ammari","year":"2016","journal-title":"Database"},{"key":"2023051607130019300_btab565-B6","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","article-title":"Laplacian eigenmaps for dimensionality reduction and data representation","volume":"15","author":"Belkin","year":"2003","journal-title":"Neural Comput"},{"key":"2023051607130019300_btab565-B7","first-page":"2787","author":"Bordes","year":"2013"},{"key":"2023051607130019300_btab565-B8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. 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