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2015 ; 2015
(ä): ä Nephropedia Template TP
gab.com Text
Twit Text FOAVip
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English Wikipedia
Construction of phosphorylation interaction networks by text mining of
full-length articles using the eFIP system
#MMPMID25833953
Tudor CO
; Ross KE
; Li G
; Vijay-Shanker K
; Wu CH
; Arighi CN
Database (Oxford)
2015[]; 2015
(ä): ä PMID25833953
show ga
Protein phosphorylation is a reversible post-translational modification where a
protein kinase adds a phosphate group to a protein, potentially regulating its
function, localization and/or activity. Phosphorylation can affect
protein-protein interactions (PPIs), abolishing interaction with previous binding
partners or enabling new interactions. Extracting phosphorylation information
coupled with PPI information from the scientific literature will facilitate the
creation of phosphorylation interaction networks of kinases, substrates and
interacting partners, toward knowledge discovery of functional outcomes of
protein phosphorylation. Increasingly, PPI databases are interested in capturing
the phosphorylation state of interacting partners. We have previously developed
the eFIP (Extracting Functional Impact of Phosphorylation) text mining system,
which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In
this work, we present several enhancements for the eFIP system: (i) text mining
for full-length articles from the PubMed Central open-access collection; (ii) the
integration of the RLIMS-P 2.0 system for the extraction of phosphorylation
events with kinase, substrate and site information; (iii) the extension of the
PPI module with new trigger words/phrases describing interactions and (iv) the
addition of the iSimp tool for sentence simplification to aid in the matching of
syntactic patterns. We enhance the website functionality to: (i) support searches
based on protein roles (kinases, substrates, interacting partners) or using
keywords; (ii) link protein entities to their corresponding UniProt identifiers
if mapped and (iii) support visual exploration of phosphorylation interaction
networks using Cytoscape. The evaluation of eFIP on full-length articles achieved
92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To
demonstrate eFIP for knowledge extraction and discovery, we constructed
phosphorylation-dependent interaction networks involving 14-3-3 proteins
identified from cancer-related versus diabetes-related articles. Comparison of
the phosphorylation interaction network of kinases, phosphoproteins and
interactants obtained from eFIP searches, along with enrichment analysis of the
protein set, revealed several shared interactions, highlighting common pathways
discussed in the context of both diseases.