Analysis of genomic and proteomic data using advanced literature mining

Yanhui Hu, Lisa M. Hines, Haifeng Weng, Dongmei Zuo, Miguel Rivera, Andrea Richardson, Joshua LaBaer

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

High-throughput technologies, such as proteomic screening and DNA micro-arrays, produce vast amounts of data requiring comprehensive analytical methods to decipher the biologically relevant results. One approach would be to manually search the biomedical literature; however, this would be an arduous task. We developed an automated literature-mining tool, termed MedGene, which comprehensively summarizes and estimates the relative strengths of all human gene-disease relationships in Medline. Using MedGene, we analyzed a novel micro-array expression dataset comparing breast cancer and normal breast tissue in the context of existing knowledge. We found no correlation between the strength of the literature association and the magnitude of the difference in expression level when considering changes as high as 5-fold; however, a significant correlation was observed (r = 0.41; p = 0.05) among genes showing an expression difference of 10-fold or more. Interestingly, this only held true for estrogen receptor (ER) positive tumors, not ER negative. MedGene identified a set of relatively understudied, yet highly expressed genes in ER negative tumors worthy of further examination.

Original languageEnglish (US)
Pages (from-to)405-412
Number of pages8
JournalJournal of Proteome Research
Volume2
Issue number4
DOIs
StatePublished - Jul 1 2003
Externally publishedYes

Keywords

  • Bioinformatics
  • Breast cancer
  • Gene-disease association
  • Micro-array
  • Text mining

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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