{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:31:54Z","timestamp":1772119914098,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772109"],"award-info":[{"award-number":["61772109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Hypothesis generation (HG) refers to the discovery of meaningful implicit connections between disjoint scientific terms, which is of great significance for drug discovery, prediction of drug side effects and precision treatment. More recently, a few initial studies attempt to model the dynamic meaning of the terms or term pairs for HG. However, most existing methods still fail to accurately capture and utilize the dynamic evolution of scientific term relations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>This article proposes a novel temporal difference embedding (TDE) learning framework to model the temporal difference information evolution of term-pair relations for predicting future interactions. Specifically, the HG problem is formulated as a future connectivity prediction task on a temporal sequence of a dynamic attributed graph. Our approach models both the local neighbor changes of the term-pairs and the changes of the global graph structure over time, learning local and global TDE of node-pairs, respectively. Future term-pair relations can be inferred in a recurrent network based on the local and global TDE. Experiments on three real-world biomedical term relationship datasets show the effectiveness and superiority of the proposed approach.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The data and source codes related to TDE are publicly available at https:\/\/github.com\/Huiweizhou\/TDE.<\/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\/btac660","type":"journal-article","created":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T12:51:24Z","timestamp":1664887884000},"page":"5253-5261","source":"Crossref","is-referenced-by-count":13,"title":["Learning temporal difference embeddings for biomedical hypothesis generation"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3065-0114","authenticated-orcid":false,"given":"Huiwei","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Dalian University of Technology , Dalian, Liaoning 116024, China"}]},{"given":"Haibin","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Dalian University of Technology , Dalian, Liaoning 116024, China"}]},{"given":"Weihong","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Dalian University of Technology , Dalian, Liaoning 116024, China"}]},{"given":"Xun","family":"Du","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Dalian University of Technology , Dalian, Liaoning 116024, China"}]}],"member":"286","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"2022113016205111800_btac660-B1","first-page":"2988","article-title":"T-pair: temporal node-pair embedding for automatic biomedical hypothesis generation","author":"Akujuobi","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"2022113016205111800_btac660-B086847475","doi-asserted-by":"crossref","first-page":"e0180539","DOI":"10.1371\/journal.pone.0180539","article-title":"Enriching plausible new hypothesis generation in pubmed","volume":"12","author":"Baek","year":"2017","journal-title":"PLoS One."},{"key":"2022113016205111800_btac660-B2","first-page":"934","author":"Derr","year":"2018"},{"key":"2022113016205111800_btac660-B3","doi-asserted-by":"crossref","first-page":"104816","DOI":"10.1016\/j.knosys.2019.06.024","article-title":"dyngraph2vec: capturing network dynamics using dynamic graph representation learning","volume":"187","author":"Goyal","year":"2020","journal-title":"Knowledge-Based Syst"},{"key":"2022113016205111800_btac660-B4","first-page":"855","author":"Grover","year":"2016"},{"key":"2022113016205111800_btac660-B5","first-page":"1025","author":"Hamilton","year":"2017"},{"key":"2022113016205111800_btac660-B6","first-page":"843","author":"Jha","year":"2019"},{"key":"2022113016205111800_btac660-B1278113","first-page":"1","article-title":"Representation learning for dynamic graphs: A survey","volume":"21","author":"Kazemi","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"2022113016205111800_btac660-B8","author":"Kim","year":"2021"},{"key":"2022113016205111800_btac660-B9","doi-asserted-by":"crossref","first-page":"4645","DOI":"10.1007\/s00500-014-1460-0","article-title":"Difference representation learning using stacked restricted boltzmann machines for change detection in sar images","volume":"20","author":"Liu","year":"2016","journal-title":"Soft Comput"},{"key":"2022113016205111800_btac660-B800","first-page":"5363","article-title":"EvolveGCN: Evolving graph convolutional networks for dynamic graphs","author":"Pareja","year":"2020","journal-title":"AAAI, New York, USA"},{"key":"2022113016205111800_btac660-B11","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.socnet.2015.02.006","article-title":"Weaving the fabric of science: dynamic network models of science\u2019s unfolding structure","volume":"43","author":"Shi","year":"2015","journal-title":"Soc. Netw"},{"key":"2022113016205111800_btac660-B12","author":"Singer","year":"2019"},{"key":"2022113016205111800_btac660-B13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0169-2607(98)00033-9","article-title":"Using arrowsmith: a computer-assisted approach to formulating and assessing scientific hypotheses","volume":"57","author":"Smalheiser","year":"1998","journal-title":"Comput. Methods Programs Biomed"},{"key":"2022113016205111800_btac660-B14","first-page":"677","author":"Srihari","year":"2007"},{"key":"2022113016205111800_btac660-B15","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1002\/asi.10389","article-title":"Text mining: generating hypotheses from medline","volume":"55","author":"Srinivasan","year":"2004","journal-title":"J. Am. Soc. Inf. Sci"},{"key":"2022113016205111800_btac660-B16","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1353\/pbm.1986.0087","article-title":"Fish oil, Raynaud\u2019s syndrome, and undiscovered public knowledge","volume":"30","author":"Swanson","year":"1986","journal-title":"Perspect. Biol. Med"},{"key":"2022113016205111800_btac660-B18","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1002\/asi.1104","article-title":"Using concepts in literature-based discovery: simulating Swanson\u2019s raynaud\u2013fish oil and migraine\u2013magnesium discoveries","volume":"52","author":"Weeber","year":"2001","journal-title":"J. Am. Soc. Inform. Sci. Technol"},{"key":"2022113016205111800_btac660-B19","doi-asserted-by":"crossref","first-page":"W587","DOI":"10.1093\/nar\/gkz389","article-title":"PubTator central: automated concept annotation for biomedical full text articles","volume":"47","author":"Wei","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022113016205111800_btac660-B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13326-015-0021-5","article-title":"Discovering relations between indirectly connected biomedical concepts","volume":"6","author":"Weissenborn","year":"2015","journal-title":"J. Biomed. Semant"},{"key":"2022113016205111800_btac660-B21","first-page":"1514","author":"Wilkowski","year":"2011"},{"key":"2022113016205111800_btac660-B22","first-page":"535","author":"Xun","year":"2017"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac660\/46571911\/btac660.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/23\/5253\/47466009\/btac660.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/23\/5253\/47466009\/btac660.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T17:26:02Z","timestamp":1669829162000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/23\/5253\/6747952"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,10,4]]},"references-count":22,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,10,4]]},"published-print":{"date-parts":[[2022,11,30]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac660","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,12,1]]},"published":{"date-parts":[[2022,10,4]]}}}