Predicting the functions of a protein from its ability to associate with other molecules

BMC Bioinformatics
Kamal Taha, Paul D Yoo

Abstract

All proteins associate with other molecules. These associated molecules are highly predictive of the potential functions of proteins. The association of a protein and a molecule can be determined from their co-occurrences in biomedical abstracts. Extensive semantically related co-occurrences of a protein's name and a molecule's name in the sentences of biomedical abstracts can be considered as indicative of the association between the protein and the molecule. Dependency parsers extract textual relations from a text by determining the grammatical relations between words in a sentence. They can be used for determining the textual relations between proteins and molecules. Despite their success, they may extract textual relations with low precision. This is because they do not consider the semantic relationships between terms in a sentence (i.e., they consider only the structural relationships between the terms). Moreover, they may not be well suited for complex sentences and for long-distance textual relations. We introduce an information extraction system called PPFBM that predicts the functions of unannotated proteins from the molecules that associate with these proteins. PPFBM represents each protein by the other molecules tha...Continue Reading

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Citations

Oct 11, 2016·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Kamal Taha

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Methods Mentioned

BETA
electrophoresis
GTPase
deubiquitination

Software Mentioned

PPFBM
KNN
LingPipe
- KNN
BioNLP
PennBio tokenizer
Berkeley
GOstruct
ConceptMapper
Apache Unstructured Information Management Architecture ( UIMA )

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