HiPub: Translating PubMed and PMC texts to networks for knowledge discovery

Kyubum Lee, Wonho Shin, Byounggun Kim, Sunwon Lee, Yonghwa Choi, Sunkyu Kim, Minji Jeon, Aik-Choon Tan, Jaewoo Kang

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2886-2888
Number of pages3
JournalBioinformatics
Volume32
Issue number18
DOIs
Publication statusPublished - 2016 Sep 15

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ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Lee, K., Shin, W., Kim, B., Lee, S., Choi, Y., Kim, S., Jeon, M., Tan, A-C., & Kang, J. (2016). HiPub: Translating PubMed and PMC texts to networks for knowledge discovery. Bioinformatics, 32(18), 2886-2888. https://doi.org/10.1093/bioinformatics/btw511