iSimp in BioC standard format: enhancing the interoperability of a sentence simplification system

Peng, Yifan; Tudor, Catalina O.; Torii, Manabu; Wu, Cathy H.; Vijay‐Shanker, K.

doi:10.1093/database/bau038

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…The impact module was described in detail in a previous publication of the eFIP system ( 2 ). Here, we will briefly summarize with examples, and concentrate on the addition of iSimp ( 4 , 5 ) for sentence simplification.…”

Section: Methodsmentioning

confidence: 99%

“…Addressing the users’ feedback, the main contributions of this work are: (i) full-scale processing of full-length articles from the PubMed Central open-access (PMC OA) database; (ii) the enhancement of the PPI module to include additional words/phrases for PPIs; (iii) the inclusion in the pipeline of iSimp, a sentence simplifier ( 4 , 5 ) to improve the recall when extracting phosphorylation–PPI relations; (iv) the incorporation of the latest version of the RLIMS-P system [RLIMS-P 2.0 ( 6 , 7 )] for phosphorylation event extraction; (v) the enhancement of the website, which allows a user to search for specific kinases, substrates, interacting proteins, keywords or lists of document IDs; (vi) the visualization of the network of interacting proteins via the Cytoscape package ( 8 ); (vii) the inclusion of gene normalization via the GenNorm system ( 9 ); (viii) an evaluation of the eFIP system on full-length articles and (ix) a corpus of annotated data from 100 randomly chosen sections from full-length documents, containing 272 unique annotations.…”

Section: Introductionmentioning

confidence: 99%

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Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system

Tudor

Ross

et al. 2015

Database

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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.Database URL: http://proteininformationresource.org/efip

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system

Tudor

Ross

et al. 2015

Database

Self Cite

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“…We first downloaded the baseline packages and then tracked the most recent updates of PubMed and PMC on a daily basis. All text in Extensible Markup Language format was transformed to BioC-JSON format, a community-driven biomedical text processing data format for improved interoperability, to simplify future data processing and exchange (36). Since sentences have a higher level of localization and information density than paragraphs, they are more likely to be relevant if they contain multiple bioentities (29).…”

Section: Text Miningmentioning

confidence: 99%

OncoPubMiner: a platform for mining oncology publications

Xu¹,

Liu

et al. 2022

Briefings in Bioinformatics

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Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop ‘keyword in–knowledge out’ oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

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“…Those techniques have been effective for detecting protein–protein interactions from text , or drug resistance information . bioSimplify and iSimp represent two popular sentence simplification approaches applied to biomedical literature, while the Cafetiere Sentence Splitter has been tested on chemical abstracts. , …”

Section: Chemical Information Retrievalmentioning

confidence: 99%

Information Retrieval and Text Mining Technologies for Chemistry

et al. 2017

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Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

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iSimp in BioC standard format: enhancing the interoperability of a sentence simplification system

Cited by 9 publications

References 8 publications

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system

OncoPubMiner: a platform for mining oncology publications

Information Retrieval and Text Mining Technologies for Chemistry

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