{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T07:38:14Z","timestamp":1768981094173,"version":"3.49.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Core Research","award":["CRG\/2022\/007706"],"award-info":[{"award-number":["CRG\/2022\/007706"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Dysregulation of a gene\u2019s function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene\u2013pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape project, researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite diverse and is confounded by several clinical and demographic covariates.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To circumvent this, we mined \u223c18 million PubMed abstracts published till May 2019 and automatically selected \u223c4.5 million of them that describe roles of particular genes in disease pathogenesis. Further, we fine-tuned the pretrained bidirectional encoder representations from transformers (BERT) for language modeling from the domain of natural language processing to learn vector representation of entities such as genes, diseases, tissues, cell-types, etc., in a way such that their relationship is preserved in a vector space. The repurposed BERT predicted disease\u2013gene associations that are not cited in the training data, thereby highlighting the feasibility of in silico synthesis of hypotheses linking different biological entities such as genes and conditions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>PathoBERT pretrained model: https:\/\/github.com\/Priyadarshini-Rai\/Pathomap-Model. BioSentVec-based abstract classification model: https:\/\/github.com\/Priyadarshini-Rai\/Pathomap-Model. Pathomap R package: https:\/\/github.com\/Priyadarshini-Rai\/Pathomap.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae185","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T19:29:34Z","timestamp":1712950174000},"source":"Crossref","is-referenced-by-count":2,"title":["Literature mining discerns latent disease\u2013gene relationships"],"prefix":"10.1093","volume":"40","author":[{"given":"Priyadarshini","family":"Rai","sequence":"first","affiliation":[{"name":"Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla Phase III , New Delhi 110020, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atishay","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla Phase III , New Delhi 110020, 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