Recognizing transcription start site (TSS) of plant promoters

Author

Loganantharaj, Raja

Author_Institution

Center for Adv. Comput. Studies, Louisiana Univ., Lafayette, LA, USA

Volume

1

fYear

2005

fDate

4-6 April 2005

Firstpage

20

Abstract

Discovering a promoter or promoters from a given DNA sequence is an active research area in bioinformatics. Many promoter detection algorithms use transcription binding sights and some core promoter elements such as CCAAT and TATA box, to determine the location of transcription start site. For the purpose of comparing the effectiveness of different algorithms, we consider transcription start site in isolation. We use annotated plant promoters for our experiments. We have compared the following algorithms for their effectiveness in detecting a TSS: position weighted matrix (PWM), naive Bayes, decision tree and artificial neural network.

Keywords

DNA; belief networks; biology computing; botany; decision trees; learning (artificial intelligence); matrix algebra; neural nets; sequences; DNA sequence; artificial neural network; bioinformatics; core promoter elements; decision tree; naive Bayes; plant promoters; position weighted matrix; promoter detection algorithms; transcription binding sights; transcription start site; Artificial neural networks; Bioinformatics; DNA; Decision trees; Detection algorithms; Machine learning algorithms; Pulse width modulation; Sequences; Software tools; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on

Print_ISBN

0-7695-2315-3

Type

conf

DOI

10.1109/ITCC.2005.240

Filename

1428431