{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:00:43Z","timestamp":1774630843053,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible method of extracting associations of SNP-phenotype from content considers their degree of certainty. In this paper, several machine learning methods were developed to extract ranked SNP-phenotype associations from biomedical abstracts and then were compared to each other. In addition, shallow machine learning methods, including random forest, logistic regression, and decision tree and two kernel-based methods like subtree and local context, a rule-based and a deep CNN-LSTM-based and two BERT-based methods were developed in this study to extract associations. Furthermore, the experiments indicated that although the used linguist features could be employed to implement a superior association extraction method outperforming the kernel-based counterparts, the used deep learning and BERT-based methods exhibited the best performance. However, the used PubMedBERT-LSTM outperformed the other developed methods among the used methods. Moreover, similar experiments were conducted to estimate the degree of certainty of the extracted association, which can be used to assess the strength of the reported association. The experiments revealed that our proposed PubMedBERT\u2013CNN-LSTM method outperformed the sophisticated methods on the task.<\/jats:p>","DOI":"10.1186\/s12859-023-05236-w","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T14:04:15Z","timestamp":1681308255000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method"],"prefix":"10.1186","volume":"24","author":[{"given":"Behrouz","family":"Bokharaeian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Dehghani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Diaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"issue":"4","key":"5236_CR1","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/70570","volume":"23","author":"GT Marth","year":"1999","unstructured":"Marth GT, et al. A general approach to single-nucleotide polymorphism discovery. Nat Genet. 1999;23(4):452\u20136.","journal-title":"Nat Genet"},{"key":"5236_CR2","unstructured":"Nature Education. 2016. \"http:\/\/www.nature.com\/scitable\/definition\/phenotype-phenotypes-35\" http:\/\/www.nature.com\/scitable\/definition\/phenotype-phenotypes-35."},{"issue":"1523","key":"5236_CR3","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1098\/rspb.2003.2372","volume":"270","author":"TD Price","year":"2003","unstructured":"Price TD, Qvarnstr A, Irwin DE. The role of phenotypic plasticity in driving genetic evolution. Proc R Soc Lond B: Biol Sci. 2003;270(1523):1433\u201340.","journal-title":"Proc R Soc Lond B: Biol Sci"},{"issue":"4","key":"5236_CR4","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1086\/383092","volume":"74","author":"S Wooding","year":"2004","unstructured":"Wooding S, Kim UK, Bamshad MJ, Larsen J, Jorde LB, Drayna D. Natural selection and molecular evolution in PTC, a bitter-taste receptor gene. Am J Hum Genet. 2004;74(4):637\u201346.","journal-title":"Am J Hum Genet"},{"issue":"1","key":"5236_CR5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s12911-016-0276-5","volume":"16","author":"K Verspoor","year":"2016","unstructured":"Verspoor K, Heo GE, Kang KY, Song M. Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts. BMC Medical Inform Decis Mak. 2016;16(1):37.","journal-title":"BMC Medical Inform Decis Mak"},{"issue":"4","key":"5236_CR6","doi-asserted-by":"publisher","first-page":"e0152725","DOI":"10.1371\/journal.pone.0152725","volume":"11","author":"M Ashique","year":"2016","unstructured":"Ashique M, Wu T-J, Mazumder R, Vijay-Shanker K. DiMeX: a text mining system for mutation-disease association extraction. PLoS ONE. 2016;11(4):e0152725.","journal-title":"PLoS ONE"},{"key":"5236_CR7","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s13326-017-0116-2","volume":"8","author":"B Bokharaeian","year":"2017","unstructured":"Bokharaeian B, Diaz A, Taghizadeh N, et al. SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature. J Biomed Semant. 2017;8:14. https:\/\/doi.org\/10.1186\/s13326-017-0116-2.","journal-title":"J Biomed Semant."},{"key":"5236_CR8","unstructured":"Loos EE, Anderson S, Day DH, Jordan PC, Wingate JD. Glossary of linguistic terms. Camp Wisdom Road Dallas: SIL International; 2004."},{"key":"5236_CR9","unstructured":"Chapman W, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. Evaluation of Negation Phrases in Narrative Clinical Reports;2002."},{"key":"5236_CR10","doi-asserted-by":"crossref","unstructured":"Bybee JL, Fleischman S. Modality in grammar and discourse. John Benjamins Publishing, vol. 32; 1995.","DOI":"10.1075\/tsl.32"},{"issue":"7","key":"5236_CR11","doi-asserted-by":"publisher","first-page":"e0200699","DOI":"10.1371\/journal.pone.0200699","volume":"13","author":"B Bhasuran","year":"2018","unstructured":"Bhasuran B, Natarajan J. Automatic extraction of gene-disease associations from literature using joint ensemble learning. PLoS ONE. 2018;13(7):e0200699. https:\/\/doi.org\/10.1371\/journal.pone.0200699.","journal-title":"PLoS ONE"},{"key":"5236_CR12","doi-asserted-by":"publisher","first-page":"6bay060","DOI":"10.1093\/database\/bay060","volume":"2018","author":"S Lim","year":"2018","unstructured":"Lim S, Kang J. Chemical-gene relation extraction using recursive neural network. Database: J Biol Databases Curation. 2018;2018:6bay060. https:\/\/doi.org\/10.1093\/database\/bay060.","journal-title":"Database: J Biol Databases Curation"},{"key":"5236_CR13","doi-asserted-by":"crossref","unstructured":"Beltagy I, Lo K, Cohan A. SciBERT: a pretrained language model for scientific text. arXiv preprint arXiv:1903.10676. 2019.","DOI":"10.18653\/v1\/D19-1371"},{"issue":"1","key":"5236_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458754","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans Comput Healthc. 2021;3(1):1\u201323.","journal-title":"ACM Trans Comput Healthc"},{"issue":"2","key":"5236_CR15","first-page":"203","volume":"4","author":"B Bokharaeian","year":"2016","unstructured":"Bokharaeian B, Diaz A. Extraction of drug\u2013drug interaction from literature through detecting linguistic-based negation and clause dependency. J AI Data Min. 2016;4(2):203\u201312.","journal-title":"J AI Data Min"},{"key":"5236_CR16","unstructured":"McDonald R. Extracting relations from unstructured text. Rapport technique, Department of Computer and Information Science-University of Pennsylvania;2005."},{"key":"5236_CR17","doi-asserted-by":"crossref","unstructured":"Ravikumar K, Liu H, Cohn JD, Wall ME, Verspoor K. Literature mining of protein-residue associations with graph rules learned through distant supervision. J Biomed Semant. 3;2012.","DOI":"10.1186\/2041-1480-3-S3-S2"},{"issue":"4","key":"5236_CR18","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1086\/383092","volume":"74","author":"S Wooding","year":"2004","unstructured":"Wooding S, et al. Natural selection and molecular evolution in PTC, a bitter-taste receptor gene. Am J Hum Genet. 2004;74(4):637\u201346.","journal-title":"Am J Hum Genet"},{"key":"5236_CR19","doi-asserted-by":"publisher","unstructured":"Alimova I, Tutubalina E. Multiple features for clinical relation extraction: a machine learning approach. J Biomed Inform. Volume 103, 2020, 103382, ISSN 1532\u20130464. https:\/\/doi.org\/10.1016\/j.jbi.2020.103382.","DOI":"10.1016\/j.jbi.2020.103382"},{"key":"5236_CR20","doi-asserted-by":"crossref","unstructured":"Mavropoulos T, Liparas D, Symeonidis S, Vrochidis S, Kompatsiaris I. A hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion. In International conference on computational linguistics and intelligent text processing 2017 (pp. 450\u2013462). Springer, Cham.","DOI":"10.1007\/978-3-319-77113-7_35"},{"key":"5236_CR21","doi-asserted-by":"crossref","unstructured":"Liu F, Zheng X, Wang B, Kiefe C. DeepGeneMD: a joint deep learning model for extracting gene mutation-disease knowledge from PubMed literature. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks 2019 (pp. 77\u201383).","DOI":"10.18653\/v1\/D19-5712"},{"key":"5236_CR22","doi-asserted-by":"crossref","unstructured":"Deng C, Zou J, Deng J, Bai M. Extraction of gene-disease association from literature using BioBERT. In The 2nd international conference on computing and data science 2021, pp. 1\u20134.","DOI":"10.1145\/3448734.3450772"},{"issue":"488","key":"5236_CR23","doi-asserted-by":"publisher","first-page":"110112","DOI":"10.1016\/j.jtbi.2019.110112","volume":"7","author":"E Nourani","year":"2020","unstructured":"Nourani E, Reshadat V. Association extraction from biomedical literature based on representation and transfer learning. J Theor Biol. 2020;7(488):110112.","journal-title":"J Theor Biol"},{"key":"5236_CR24","doi-asserted-by":"crossref","unstructured":"Lee K, Wei CH, Lu Z. Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature. Brief Bioinform. 2021;22(3):bbaa142.","DOI":"10.1093\/bib\/bbaa142"},{"issue":"12","key":"5236_CR25","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1093\/bioinformatics\/btaa907","volume":"37","author":"M Asada","year":"2021","unstructured":"Asada M, Miwa M, Sasaki Y. Using drug descriptions and molecular structures for drug-drug interaction extraction from literature. Bioinformatics. 2021;37(12):1739\u201346. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa907","journal-title":"Bioinformatics"},{"key":"5236_CR26","doi-asserted-by":"publisher","first-page":"101260","DOI":"10.1109\/ACCESS.2019.2930641","volume":"7","author":"J Liu","year":"2019","unstructured":"Liu J, Huang Z, Ren F, Hua L. Drug\u2013drug interaction extraction based on transfer weight matrix and memory network. IEEE Access. 2019;7:101260\u20138.","journal-title":"IEEE Access"},{"key":"5236_CR27","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s13326-021-00248-y","volume":"12","author":"J Legrand","year":"2021","unstructured":"Legrand J, Toussaint Y, Ra\u00efssi C, et al. Syntax-based transfer learning for the task of biomedical relation extraction. J Biomed Semant. 2021;12:16.","journal-title":"J Biomed Semant"},{"key":"5236_CR28","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s12859-021-04534-5","volume":"23","author":"J Chen","year":"2022","unstructured":"Chen J, Hu B, Peng W, et al. Biomedical relation extraction via knowledge-enhanced reading comprehension. BMC Bioinform. 2022;23:20.","journal-title":"BMC Bioinform"},{"issue":"1","key":"5236_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2029-1","volume":"19","author":"K Lee","year":"2018","unstructured":"Lee K, Kim B, Choi Y, Kim S, Shin W, Lee S, Park S, Kim S, Tan AC, Kang J. Deep learning of mutation-gene-drug relations from the literature. BMC Bioinform. 2018;19(1):1\u20133.","journal-title":"BMC Bioinform"},{"key":"5236_CR30","unstructured":"Chowdhury MFM, Lavelli A. Exploiting the scope of negations and heterogeneous features for relation extraction: a case study for drug\u2013drug interaction extraction. In HLT-NAACL13, 2013;765\u201371."},{"key":"5236_CR31","doi-asserted-by":"crossref","unstructured":"Pyysalo S, Airola A, Heimonen J, Bj\u00f6rne J, Ginter F, Salakoski T. Comparative analysis of five protein\u2013protein interaction corpora. BMC Bioinform. 2008;9(3):S6.","DOI":"10.1186\/1471-2105-9-S3-S6"},{"key":"5236_CR32","doi-asserted-by":"crossref","unstructured":"Chek Kim, L, and Miin-Hwa Lim, J..\"Hedging in Academic Writing - A Pedagogically-Motivated Qualitative Study ,\" Procedia - Social and Behavioral Sciences , vol. 197, pp. 600\u2013607, 2015, 7th World Conference on Educational Sciences. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877042815042019. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877042815042019","DOI":"10.1016\/j.sbspro.2015.07.200"},{"key":"5236_CR33","unstructured":"Thorsten J. Making large scale SVM learning practical. Universitat Dortmund, Tech. rep.;1999."},{"key":"5236_CR34","doi-asserted-by":"publisher","unstructured":"Song, B. et al. Classification of imbalanced oral cancer image data from high-risk population. J Biomed Opt. 26,10 (2021): 105001. doi:https:\/\/doi.org\/10.1117\/1.JBO.26.10.105001","DOI":"10.1117\/1.JBO.26.10.105001"},{"issue":"10","key":"5236_CR35","doi-asserted-by":"publisher","first-page":"e0163480","DOI":"10.1371\/journal.pone.0163480","volume":"11","author":"B Bokharaeian","year":"2016","unstructured":"Bokharaeian B, Diaz A, Chitsaz H. Enhancing extraction of drug-drug interaction from literature using neutral candidates, negation, and clause dependency. PLoS ONE. 2016;11(10):e0163480.","journal-title":"PLoS ONE"},{"key":"5236_CR36","doi-asserted-by":"crossref","unstructured":"Deng C, Zou J, Deng J, Bai M. Extraction of gene-disease association from literature using BioBERT. In The 2nd international conference on computing and data science, pp. 1\u20134; 2021.","DOI":"10.1145\/3448734.3450772"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05236-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05236-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05236-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T14:05:11Z","timestamp":1681308311000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05236-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,12]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5236"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05236-w","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,12]]},"assertion":[{"value":"1 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"144"}}