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 journalArticlepeer-review

14 Citations (Scopus)


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
Issue number18
Publication statusPublished - 2016 Sept 15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea. NRF- 2014M3C9A3063543 (to J.K.), NRF-2014R1A2A1A10051238 (to J.K.) and NRF-2015M3A9D7031070 (to J.K.), the National Institutes of Health P50CA058187 (to A.C.T.), P30CA046934 (to A.C.T.) and the David F. and Margaret T. Grohne Family Foundation (to A.C.T.).

Publisher Copyright:
© 2016 The Author. Published by Oxford University Press. All rights reserved.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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