Twitter K-H networks in action: Advancing biomedical literature for drug search

J Biomed Inform. 2015 Aug;56:157-68. doi: 10.1016/j.jbi.2015.05.015. Epub 2015 Jun 8.

Abstract

The importance of searching biomedical literature for drug interaction and side-effects is apparent. Current digital libraries (e.g., PubMed) suffer infrequent tagging and metadata annotation updates. Such limitations cause absence of linking literature to new scientific evidence. This demonstrates a great deal of challenges that stand in the way of scientists when searching biomedical repositories. In this paper, we present a network mining approach that provides a bridge for linking and searching drug-related literature. Our contributions here are two fold: (1) an efficient algorithm called HashPairMiner to address the run-time complexity issues demonstrated in its predecessor algorithm: HashnetMiner, and (2) a database of discoveries hosted on the web to facilitate literature search using the results produced by HashPairMiner. Though the K-H network model and the HashPairMiner algorithm are fairly young, their outcome is evidence of the considerable promise they offer to the biomedical science community in general and the drug research community in particular.

Keywords: Drugs; K-H networks; Mining; PubMed; Search; Twitter.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Automation
  • Data Collection
  • Data Mining / methods*
  • Database Management Systems
  • Drug Design*
  • Drug Industry / methods
  • Drug Interactions
  • Internet
  • Medical Subject Headings
  • Pharmaceutical Preparations / chemistry
  • PubMed
  • Social Media*
  • Software

Substances

  • Pharmaceutical Preparations