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Autism spectrum disorder (ASD) is a disease related to a neurological and developmental disorder characterized by deficits in communication and social interaction and by repetitive behaviour. However, this kind of disease remains unclear to date. In this study, it identifies entities associated with disease using the machine learning of a computational way from text data collection for molecular mechanisms related to ASD. Entities related to disease are extracted from the biomedical literature related to autism by using deep learning with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) model. Compared other previous works, the approach is promising for identifying entities related to disease. The proposed approach including five types of molecular entities is evaluated by GENIA corpus to obtain an F-score of 76.81%. The work has extracted 9146 proteins, 145 RNAs, 7680 DNAs, 1058 cell-types, and 981 cell-lines from the autism biomedical literature after removing repeated molecular entities. Finally, we perform GO and KEGG analyses of the test dataset. This study could serve as a reference for further studies on the etiology of disease on the basis of molecular mechanisms and provide a way to explore disease genetic information.<\/jats:p>","DOI":"10.1155\/2021\/6635027","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T03:04:43Z","timestamp":1613790283000},"page":"1-8","source":"Crossref","is-referenced-by-count":11,"title":["Recognition of Disease Genetic Information from Unstructured Text Data Based on BiLSTM-CRF for Molecular Mechanisms"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7971-3000","authenticated-orcid":true,"given":"Lejun","family":"Gong","sequence":"first","affiliation":[{"name":"Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Zhejiang Engineering Research Center of Intelligent Medicine, Wenzhou 325035, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingxing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyin","family":"Chen","sequence":"additional","affiliation":[{"name":"Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ins.2019.03.023","article-title":"Deep rolling: a novel emotion prediction model for a multi-participant communication context","volume":"488","author":"R. 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