title: Promoter recognition based on the maximum entropy hidden Markov model.
abstract: Since the fast development of genome sequencing has produced large scale data, the current work uses the bioinformatics methods to recognize different gene regions, such as exon, intron and promoter, which play an important role in gene regulations. In this paper, we introduce a new method based on the maximum entropy Markov model (MEMM) to recognize the promoter, which utilizes the biological features of the promoter for the condition. However, it leads to a high false positive rate (FPR). In order to reduce the FPR, we provide another new method based on the maximum entropy hidden Markov model (ME-HMM) without the independence assumption, which could also accommodate the biological features effectively. To demonstrate the precision, the new methods are implemented by R language and the hidden Markov model (HMM) is introduced for comparison. The experimental results show that the new methods may not only overcome the shortcomings of HMM, but also have their own advantages. The results indicate that, MEMM is excellent for identifying the conserved signals, and ME-HMM can demonstrably improve the true positive rate.
authors.affiliation: College of Science, Nanjing Agricultural University, China.
authors.forename: Xiao-yu
authors.initials: XY
authors.lastname: Zhao
authors.affiliation: College of Science, Nanjing Agricultural University, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Science, Nanjing Agricultural University, China.
authors.forename: Yuan-yuan
authors.initials: YY
authors.lastname: Chen
authors.affiliation: College of Science, Nanjing Agricultural University, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.affiliation: College of Science, Nanjing Agricultural University, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Yang
authors.affiliation: College of Science, Nanjing Agricultural University, China.
authors.forename: Cong
authors.initials: C
authors.lastname: Pian
authors.affiliation: College of Science, Nanjing Agricultural University, China. Electronic address: zlyun@njau.edu.cn.
authors.forename: Liang-yun
authors.initials: LY
authors.lastname: Zhang
mesh_terms: D030561:Databases, Nucleic Acid; D019277:Entropy; D008390:Markov Chains; D011237:Predictive Value of Tests; D011401:Promoter Regions, Genetic / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: Forward algorithm; IIS algorithm; ME-HMM; MEMM; Promoter