HiPub: translating PubMed and PMC texts to networks for knowledge discovery
Author(s) -
Kyubum Lee,
WonHo Shin,
Byounggun Kim,
Sunwon Lee,
Yonghwa Choi,
Sunkyu Kim,
Minji Jeon,
Aik Choon Tan,
Jaewoo Kang
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw511
Subject(s) - computer science , construct (python library) , context (archaeology) , entity linking , information retrieval , knowledge extraction , world wide web , knowledge base , artificial intelligence , biology , paleontology , programming language
We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations.
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