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Semantic rules for extracting proteins functions information from biomedical abstracts

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a classifier system called SRPFP that predicts the functions of un-annotated proteins. SRPFP aims at enhancing the state of the art of biological text mining. It analyzes biomedical texts in order to discover protein function information that is difficult to retrieve. It employs semantic rules for extracting proteins functions information from biomedical abstracts. It applies a novel model and linguistic computational techniques for extracting the functional relationship from different structural forms of terms in the sentences of biological abstracts. Specifically, SRPFP extracts phrases that represent functional relationships between proteins and molecules. These molecules usually bind to the proteins and are highly predictive of the functions of these proteins. The proposed semantic rules can identify the semantic relationship between each co-occurrence of a protein-molecule pair using the syntactic structures of sentences and linguistics theories. SRPFP represents each protein by the molecules that have high co-occurrences with the protein in biomedical abstracts. This is because such molecules are good characteristics and indicators of the functions of proteins. SRPFP measures the semantic similarity between the molecules representing an un-annotated protein p and the molecules representing annotated proteins and assigns p the functions of annotated proteins that are similar to p.

Original languageBritish English
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages595-598
Number of pages4
ISBN (Electronic)9781467367981
DOIs
StatePublished - 16 Dec 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: 9 Nov 201512 Nov 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/TerritoryUnited States
CityWashington
Period9/11/1512/11/15

Keywords

  • biological NLP
  • biomedical literature
  • Dependency parser
  • Information extraction
  • Text mining

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