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 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 approximately 66% in sensitivity and approximately 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.
Chen
Chuan-Bo
CB
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China. chuanboc@163.com
Li
Tao
T
D019295:Computational Biology / Q000379:methods*; D018899:CpG Islands / Q000235:genetics; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D006801:Humans; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic / Q000235:genetics*