{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:59:02Z","timestamp":1772783942026,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wellcome Trust\/DBT India Alliance Intermediate Fellowship","award":["IA\/I\/17\/2\/503323"],"award-info":[{"award-number":["IA\/I\/17\/2\/503323"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Inter-organ\/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present a first study to predict from biomedical literature the hormone\u2013gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS* models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone\u2013gene pair is associated or not, and whether an associated gene is involved in the hormone\u2019s production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue\u2013tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Freely available at https:\/\/cross-tissue-signaling.herokuapp.com are our model predictions &amp; datasets; https:\/\/github.com\/BIRDSgroup\/BioEmbedS has all relevant code.<\/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\/btac578","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T09:35:29Z","timestamp":1661333729000},"page":"4771-4781","source":"Crossref","is-referenced-by-count":8,"title":["Predicting cross-tissue hormone\u2013gene relations using balanced word embeddings"],"prefix":"10.1093","volume":"38","author":[{"given":"Aditya","family":"Jadhav","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras , Chennai, India"}]},{"given":"Tarun","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras , Chennai, India"},{"name":"Initiative for Biological Systems Engineering, IIT Madras , Chennai, India"},{"name":"Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras , Chennai, India"}]},{"given":"Mohit","family":"Raghavendra","sequence":"additional","affiliation":[{"name":"Department of Information Technology, National Institute of Technology Karnataka , Surathkal, India"}]},{"given":"Tamizhini","family":"Loganathan","sequence":"additional","affiliation":[{"name":"Initiative for Biological Systems Engineering, IIT Madras , Chennai, India"},{"name":"Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras , Chennai, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8490-4087","authenticated-orcid":false,"given":"Manikandan","family":"Narayanan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras , Chennai, India"},{"name":"Initiative for Biological Systems Engineering, IIT Madras , Chennai, India"},{"name":"Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras , Chennai, India"}]}],"member":"286","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"2022101415174294100_btac578-B1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s41574-018-0123-0","article-title":"Inter-tissue communication in cancer cachexia","volume":"15","author":"Argil\u00e9s","year":"2018","journal-title":"Nat. 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