title: [Predicting human tumor-specific promoter using transcription factor binding sites].
abstract: To predict human tumor-specific promoters using computer technology, three data sets of tumor-specific promoter sequences, transcription factor binding site sequences and non-tumor promoter sequences were collected, and the tumor-specific promoter sequences and non-tumor promoter sequences were analyzed for the comparative density of each unique transcription factor binding site. The density of each transcription factor binding site in the corresponding data set was used to derive the density ratio, from which the eigenvector was constructed to identify human tumor-specific promoters. This method proved to be highly accurate in predicting the tumor-specific promoters, with recognition rates of over 90% of the training sequences and over 80% of the testing sequences.
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. king@fimmu.com
authors.forename: Qi
authors.initials: Q
authors.lastname: Wang
authors.forename: Wei-peng
authors.initials: WP
authors.lastname: Li
authors.forename: Zhong-xi
authors.initials: ZX
authors.lastname: Huang
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D004268:DNA-Binding Proteins / Q000378:metabolism; D001330:Electronic Data Processing; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D006801:Humans; D011401:Promoter Regions, Genetic / Q000235:genetics*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D004268:DNA-Binding Proteins; D014157:Transcription Factors