RLIMS-P 2.0: A Generalizable Rule-Based Information Extraction System for Literature Mining of Protein Phosphorylation Information

Overview

Journal IEEE/ACM Trans Comput Biol Bioinform

Specialty Biology

Date 2015 Sep 11

PMID 26357075

Citations 27

Authors

Manabu Torii

Cecilia N Arighi

Gang Li

Qinghua Wang

Cathy H Wu

K Vijay-Shanker

Affiliations

Soon will be listed here.

Abstract

We introduce RLIMS-P version 2.0, an enhanced rule-based information extraction (IE) system for mining kinase, substrate, and phosphorylation site information from scientific literature. Consisting of natural language processing and IE modules, the system has integrated several new features, including the capability of processing full-text articles and generalizability towards different post-translational modifications (PTMs). To evaluate the system, sets of abstracts and full-text articles, containing a variety of textual expressions, were annotated. On the abstract corpus, the system achieved F-scores of 0.91, 0.92, and 0.95 for kinases, substrates, and sites, respectively. The corresponding scores on the full-text corpus were 0.88, 0.91, and 0.92. It was additionally evaluated on the corpus of the 2013 BioNLP-ST GE task, and achieved an F-score of 0.87 for the phosphorylation core task, improving upon the results previously reported on the corpus. Full-scale processing of all abstracts in MEDLINE and all articles in PubMed Central Open Access Subset has demonstrated scalability for mining rich information in literature, enabling its adoption for biocuration and for knowledge discovery. The new system is generalizable and it will be adapted to tackle other major PTM types. RLIMS-P 2.0 online system is available online (http://proteininformationresource.org/rlimsp/) and the developed corpora are available from iProLINK (http://proteininformationresource.org/iprolink/).

Citing Articles

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References

Cohen K, Johnson H, Verspoor K, Roeder C, Hunter L . The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinformatics. 2010; 11:492. PMC: 3098079. DOI: 10.1186/1471-2105-11-492. View

Veuthey A, Bridge A, Gobeill J, Ruch P, McEntyre J, Bougueleret L . Application of text-mining for updating protein post-translational modification annotation in UniProtKB. BMC Bioinformatics. 2013; 14:104. PMC: 3660268. DOI: 10.1186/1471-2105-14-104. View

Hirschman L, Park J, Tsujii J, Wong L, Wu C . Accomplishments and challenges in literature data mining for biology. Bioinformatics. 2002; 18(12):1553-61. DOI: 10.1093/bioinformatics/18.12.1553. View

Hornbeck P, Kornhauser J, Tkachev S, Zhang B, Skrzypek E, Murray B . PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res. 2011; 40(Database issue):D261-70. PMC: 3245126. DOI: 10.1093/nar/gkr1122. View

Rzhetsky A, Seringhaus M, Gerstein M . Seeking a new biology through text mining. Cell. 2008; 134(1):9-13. PMC: 2735884. DOI: 10.1016/j.cell.2008.06.029. View

Van Landeghem S, Bjorne J, Abeel T, De Baets B, Salakoski T, Van de Peer Y . Semantically linking molecular entities in literature through entity relationships. BMC Bioinformatics. 2012; 13 Suppl 11:S6. PMC: 3384255. DOI: 10.1186/1471-2105-13-S11-S6. View

Wu C, Nebert D . Update on genome completion and annotations: Protein Information Resource. Hum Genomics. 2004; 1(3):229-33. PMC: 3525084. DOI: 10.1186/1479-7364-1-3-229. View

De Bruijn B, Martin J . Getting to the (c)ore of knowledge: mining biomedical literature. Int J Med Inform. 2002; 67(1-3):7-18. DOI: 10.1016/s1386-5056(02)00050-3. View

Hu Z, Narayanaswamy M, Ravikumar K, Vijay-Shanker K, Wu C . Literature mining and database annotation of protein phosphorylation using a rule-based system. Bioinformatics. 2005; 21(11):2759-65. DOI: 10.1093/bioinformatics/bti390. View

10.

Pyysalo S, Ohta T, Rak R, Sullivan D, Mao C, Wang C . Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011. BMC Bioinformatics. 2012; 13 Suppl 11:S2. PMC: 3384257. DOI: 10.1186/1471-2105-13-S11-S2. View

11.

Krallinger M, Valencia A, Hirschman L . Linking genes to literature: text mining, information extraction, and retrieval applications for biology. Genome Biol. 2008; 9 Suppl 2:S8. PMC: 2559992. DOI: 10.1186/gb-2008-9-s2-s8. View

12.

Ross K, Arighi C, Ren J, Natale D, Huang H, Wu C . Use of the protein ontology for multi-faceted analysis of biological processes: a case study of the spindle checkpoint. Front Genet. 2013; 4:62. PMC: 3636526. DOI: 10.3389/fgene.2013.00062. View

13.

Van Landeghem S, de Bodt S, Drebert Z, Inze D, Van de Peer Y . The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis. Plant Cell. 2013; 25(3):794-807. PMC: 3634689. DOI: 10.1105/tpc.112.108753. View

14.

Ross K, Arighi C, Ren J, Huang H, Wu C . Construction of protein phosphorylation networks by data mining, text mining and ontology integration: analysis of the spindle checkpoint. Database (Oxford). 2013; 2013:bat038. PMC: 3675891. DOI: 10.1093/database/bat038. View

15.

Hu Z, Mani I, Hermoso V, Liu H, Wu C . iProLINK: an integrated protein resource for literature mining. Comput Biol Chem. 2004; 28(5-6):409-16. DOI: 10.1016/j.compbiolchem.2004.09.010. View

16.

Kilicoglu H, Bergler S . Biological event composition. BMC Bioinformatics. 2012; 13 Suppl 11:S7. PMC: 3384260. DOI: 10.1186/1471-2105-13-S11-S7. View

17.

Blaschke C, Valencia A . The Functional Genomics Network in the evolution of biological text mining over the past decade. N Biotechnol. 2012; 30(3):278-85. DOI: 10.1016/j.nbt.2012.11.020. View

18.

Stark C, Su T, Breitkreutz A, Lourenco P, Dahabieh M, Breitkreutz B . PhosphoGRID: a database of experimentally verified in vivo protein phosphorylation sites from the budding yeast Saccharomyces cerevisiae. Database (Oxford). 2010; 2010:bap026. PMC: 2860897. DOI: 10.1093/database/bap026. View

19.

Rodriguez-Esteban R . Biomedical text mining and its applications. PLoS Comput Biol. 2009; 5(12):e1000597. PMC: 2791166. DOI: 10.1371/journal.pcbi.1000597. View

20.

Dinkel H, Chica C, Via A, Gould C, Jensen L, Gibson T . Phospho.ELM: a database of phosphorylation sites--update 2011. Nucleic Acids Res. 2010; 39(Database issue):D261-7. PMC: 3013696. DOI: 10.1093/nar/gkq1104. View