A hybrid neural network system for prediction and recognition of promoter regions in human genome

This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence,feeding these features as input for a hybrid neural network syst...

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Bibliographic Details
Published in:Journal of Zhejiang University. B. Science 2005-05, Vol.6 (5), p.401-407
Main Author: 陈传波 李滔
Format: Article
Language:English
Subjects:
Algorithms
Biotechnology
Chromosome 22
Computational Biology - methods
CpG islands
CpG Islands - genetics
DNA
Feature extraction
Genome, Human
Genomics - methods
Humans
Hybrid systems
Machine learning
Matlab
Neural networks
Neural Networks (Computer)
Nucleotide sequence
Promoter Regions, Genetic - genetics
分子生物学
基因组
神经网络系统
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Summary:This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence,feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm.Evaluation on Human chromosome 22 was -66% in sensitivity and -48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.
ISSN:1673-1581
1862-1783
DOI:10.1631/jzus.2005.B0401