Promoter Prediction of Prokaryotes

reportActive / Technical Report | Accesssion Number: AD1020017 | Open PDF

Abstract:

The availability of computational methods to identify and define the precise structure and location of promoters in prokaryotic genomes will provide a critical firststep towards understanding the mechanisms by which genes are organized and regulated. We examine three different methods for promoter identification, two ofwhich are adopted from related work and the other is a novel approach based on feature extraction. By the results of a set of experiments we evaluated predictionaccuracy for identifying promoter regions from non-coding regions.

Author(s):
Kuramochi, Michihiro ; Deshpande, Mukund ; Karypis, George ; Zhang, Qing ; Kapur, Vivek
Author Organization(s):
University of Minnesota
Funding Organization(s):
Publication Date:
2001 Jul 23
Pagination:
0

Security Markings

RECORD

Collection: TRECMS
Subject Terms
Modernization Areas:
AI and Machine Learning
Communities of Interest:
No COI(s) Identified
Descriptor(s):
supervised machine learning, computational science, data sets, identification, computer science, machine learning, computer programming, precision, probability, recognition, accuracy, artificial intelligence, frequency, nucleotides, prokaryotes, sequences, algorithms
Keyword(s):
prokaryotes, genome sequencing, gene finding algorthims, RNA transcription, promoter location prediction
Subject Categories:
Biological and Medical Sciences; Mathematical and Computer Sciences; Biological and Medical Sciences
Update Date:
2018 May 30