NDLI: Statistical Approaches to Identifying Androgen Response Elements

Content Provider	IEEE Xplore Digital Library
Author	Li, Li Heber, Steffen Zhang, Qiang Andersen, Melvin E.
Copyright Year	2007
Abstract	DNA-binding transcription factors play an integral role in regulating gene expression. Transcription factor binding sites (TFBS) in the gene promoter regions can be predicted by using computational methods, such as Support Vector Machine (SVM), Hidden Markov Model (HMM), and Random Forest (RF), all of which summarize sequence patterns of experimentally determined TFBSs. Androgen receptor (AR), a ligand-dependent transcription factor, plays an important role in male reproductive functions by regulating gene transcription through directly binding to androgen response elements (ARE) in target gene promoters. The aim of this study is to use data mining tools to identify and characterize AREs based on sequence information. Three statistical methods were explored to strengthen the prediction of putative AREs in the human genome. Cross-validation results indicated that all of the three models provided good sensitivity and specificity in identifying AREs, with an accuracy of at least 80%. It is the first time that HMM, SVM and RF have all been applied to constructing ARE prediction models.
Starting Page	95
Ending Page	100
File Size	283067
Page Count	6
File Format	PDF
ISBN	9780769530192
DOI	10.1109/ICDMW.2007.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Radio frequency Statistical analysis Hidden Markov models Humans Genomics Gene expression Data mining Bioinformatics Radiofrequency identification
Content Type	Text
Resource Type	Article

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