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Analysis of genomic and proteomic data using advanced literature mining
Yanhui Hu1, Lisa M Hines, Haifeng Weng
1Institute of Proteomics, Harvard Medical School-BCMP, 240 Longwood Avenue, Boston, Massachusetts 02115, USA.
Journal of Proteome Research
|August 27, 2003
Summary
MedGene, an automated tool, analyzes gene-disease relationships from biomedical literature. It found significant correlations between gene expression and disease association in breast cancer, particularly for estrogen receptor-positive tumors with large expression differences.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- High-throughput technologies generate large datasets requiring advanced analytical methods.
- Manual literature review for gene-disease relationships is time-consuming and impractical.
Purpose of the Study:
- To develop an automated literature-mining tool (MedGene) for analyzing human gene-disease relationships.
- To apply MedGene to a novel microarray dataset of breast cancer tissues to identify biologically relevant gene-disease associations.
Main Methods:
- Development of MedGene, an automated tool to summarize and quantify human gene-disease relationships from the Medline database.
- Analysis of a microarray expression dataset comparing breast cancer and normal breast tissue using MedGene.
- Correlation analysis between gene expression differences and literature-based association strengths.
Main Results:
- A significant correlation (r = 0.41; p = 0.05) was observed between gene expression difference (≥10-fold) and literature association strength in breast cancer.
- This correlation was specific to estrogen receptor (ER) positive tumors, not ER negative.
- MedGene identified understudied, highly expressed genes in ER negative tumors warranting further investigation.
Conclusions:
- Automated literature mining tools like MedGene can effectively integrate high-throughput data with existing knowledge.
- Gene expression patterns in breast cancer are significantly associated with known gene-disease relationships, especially in ER-positive cases with substantial expression changes.
- MedGene highlights potential novel research avenues by identifying key genes in ER-negative tumors.