title: Analysis of genomic and proteomic data using advanced literature mining.
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.
authors.affiliation: Institute of Proteomics, Harvard Medical School-BCMP, 240 Longwood Avenue, Boston, Massachusetts 02115, USA.
authors.forename: Yanhui
authors.initials: Y
authors.lastname: Hu
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Hines
authors.forename: Haifeng
authors.initials: H
authors.lastname: Weng
authors.forename: Dongmei
authors.initials: D
authors.lastname: Zuo
authors.forename: Miguel
authors.initials: M
authors.lastname: Rivera
authors.forename: Andrea
authors.initials: A
authors.lastname: Richardson
authors.forename: Joshua
authors.initials: J
authors.lastname: LaBaer
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods; D000465:Algorithms; D001161:Arteriosclerosis / Q000235:genetics; D001172:Arthritis, Rheumatoid / Q000235:genetics; D001714:Bipolar Disorder / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D016206:Databases, Bibliographic; D030541:Databases, Genetic; D004194:Disease / Q000145:classification; D001330:Electronic Data Processing / Q000379:methods; D005260:Female; D015870:Gene Expression; D023281:Genomics; D006801:Humans; D006973:Hypertension / Q000235:genetics; D016239:MEDLINE; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D040901:Proteomics; D011959:Receptors, Estradiol / Q000235:genetics; D012986:Software Validation
chemical_list: D011959:Receptors, Estradiol