PromPredictor: a hybrid machine learning system for recognition and location of transcription start sites in human genome

Authors:
Tao Li;Chuanbo Chen
Affiliations:
College of Computer Science & Technology, Huazhong University of Science & Technology, Wuhan, China;College of Computer Science & Technology, Huazhong University of Science & Technology, Wuhan, China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

In this paper we present a novel hybrid machine learning system for recognition of gene starts in human genome. The system makes predictions of gene start by extracting compositional features and CpG islands information from promoter regions. It combines a new promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm. Evaluation on Human chromosome 4, 21, 22 was 64.47% in sensitivity and 82.20% in specificity. Comparison with the three other systems revealed that our system had superior sensitivity and specificity in predicting gene starts. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at www.whtelecom.com/Prompredictor.htm.