{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:38:52Z","timestamp":1740184732821,"version":"3.37.3"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0910404"],"award-info":[{"award-number":["2018YFC0910404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772409"],"award-info":[{"award-number":["61772409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019069","name":"Chinese Academy of Engineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019069","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of China Knowledge Centre for Engineering Science and Technology"},{"name":"Innovation Team from the Ministry of Education","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61721002"],"award-info":[{"award-number":["61721002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Professor Chen Li\u2019 s Recruitment Program for Young Professionals of \u2018The Thousand Talents Plan\u2019"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein\u2013protein interactions and gene\u2013disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is available at the gitlab repository https:\/\/gitlab.com\/BioAI\/erbk.<\/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\/btaa913","type":"journal-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T12:36:45Z","timestamp":1602592605000},"page":"1156-1163","source":"Crossref","is-referenced-by-count":2,"title":["A representation model for biological entities by fusing structured axioms with unstructured texts"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6683-0256","authenticated-orcid":false,"given":"Peiliang","family":"Lou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710049, China"},{"name":"Key Laboratory of Intelligent Networks and Network Security (Xi\u2019an Jiaotong University), Ministry of Education, Xi\u2019an , Shaanxi 710049, China"}]},{"given":"YuXin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710049, China"}]},{"given":"Antonio","family":"Jimeno Yepes","sequence":"additional","affiliation":[{"name":"IBM Research Australia , Southbank, VIC 3006, Australia"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710049, China"},{"name":"National Engineering Lab for Big Data Analytics, Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710049, China"}]}],"member":"286","published-online":{"date-parts":[[2020,10,23]]},"reference":[{"key":"2023051612063264000_btaa913-B1","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":"2023051612063264000_btaa913-B2","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":"2023051612063264000_btaa913-B3","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","author":"Bordes","year":"2013"},{"key":"2023051612063264000_btaa913-B4","doi-asserted-by":"crossref","first-page":"i305","DOI":"10.1093\/bioinformatics\/btz328","article-title":"Multifaceted protein\u2013protein interaction prediction based on Siamese residual RCNN","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051612063264000_btaa913-B5","first-page":"D330","article-title":"The gene ontology resource: 20 years and still going strong","volume":"47","author":"Consortium","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023051612063264000_btaa913-B6","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.1093\/nar\/gky092","article-title":"Uniprot: the universal protein knowledgebase","volume":"46","author":"Consortium","year":"2018","journal-title":"Nucleic Acids Res"},{"year":"2018","author":"De Cao","key":"2023051612063264000_btaa913-B7"},{"year":"2018","author":"Devlin","key":"2023051612063264000_btaa913-B8"},{"key":"2023051612063264000_btaa913-B9","doi-asserted-by":"crossref","first-page":"D481","DOI":"10.1093\/nar\/gkv1351","article-title":"The reactome pathway knowledgebase","volume":"44","author":"Fabregat","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023051612063264000_btaa913-B10","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.websem.2008.05.001","article-title":"OWL 2: the next step for owl","volume":"6","author":"Grau","year":"2008","journal-title":"Web Semant. 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