Analysis of Genomic and Proteomic Data Using Advanced Literature Mining

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...

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Bibliographic Details
Published in:Journal of proteome research 2003-07, Vol.2 (4), p.405-412
Main Authors: Hu, Yanhui, Hines, Lisa M, Weng, Haifeng, Zuo, Dongmei, Rivera, Miguel, Richardson, Andrea, LaBaer, Joshua
Format: Article
Language:English
Subjects:
Abstracting and Indexing as Topic - methods
Algorithms
Arteriosclerosis - genetics
Arthritis, Rheumatoid - genetics
Automatic Data Processing - methods
Bipolar Disorder - genetics
Breast Neoplasms - genetics
Cluster Analysis
Computational Biology - methods
Data Interpretation, Statistical
Databases, Bibliographic
Databases, Genetic
Disease - classification
Female
Gene Expression
Genomics
Humans
Hypertension - genetics
MEDLINE
Oligonucleotide Array Sequence Analysis - methods
Proteomics
Receptors, Estradiol - genetics
Software Validation
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Summary: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. Keywords: bioinformatics • micro-array • text mining • gene-disease association • breast cancer
ISSN:1535-3893
1535-3907
DOI:10.1021/pr0340227