Multilevel text mining for bone biology

Omkar Tilak, Andrew Hoblitzell, Snehasis Mukhopadhyay, Qian You, Shiaofen Fang, Yuni Xia, Joseph Bidwell

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results.

Original languageEnglish (US)
Pages (from-to)2355-2364
Number of pages10
JournalConcurrency Computation Practice and Experience
Volume23
Issue number17
DOIs
StatePublished - Dec 10 2011

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Keywords

  • artificial intelligence
  • biological literature
  • bone biology
  • network flow
  • text mining
  • transitive closure

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Tilak, O., Hoblitzell, A., Mukhopadhyay, S., You, Q., Fang, S., Xia, Y., & Bidwell, J. (2011). Multilevel text mining for bone biology. Concurrency Computation Practice and Experience, 23(17), 2355-2364. https://doi.org/10.1002/cpe.1788