Analysis of genomic and proteomic data using advanced literature mining

被引:59
作者
Hu, YH [1 ]
Hines, LM [1 ]
Weng, HF [1 ]
Zuo, DM [1 ]
Rivera, M [1 ]
Richardson, A [1 ]
LaBaer, J [1 ]
机构
[1] Harvard Univ, Sch Med, Inst Prote, BCMP, Boston, MA 02115 USA
关键词
bioinformatics; micro-array; text mining; gene-disease association; breast cancer;
D O I
10.1021/pr0340227
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:405 / 412
页数:8
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