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Machine Learning-Based Approaches Identify a Key Physicochemical Property for Accurately Predicting Polyadenlylation Signals in Genomic Sequences

  • HaiBo Cui
  • Jia Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)

Abstract

Accurately predicting poly(A) signals (PASs) is one of important topics in bioinformatics for high-quality genome annotation and transcription regulation mechanism investigation. In this study, we identified a powerful physicochemical property of DNA sequence for computationally predicting PASs using machine learning technologies. On the basis of this feature, we built a PAS prediction model by capturing the position-specific information from the region surrounding PASs. The cross-validation results demonstrated that the prediction accuracies of our constructed model on 12 categories of human PASs are comparable to those of recently published PAS predictor Dragon PolyA Spotter. Further analysis revealed that the region 25 nucleotides downstream of PASs is the most important region for the accurate prediction of PASs.

Keywords

polyadenlylation site poly(A) genomic sequence physicochemical property dinucleotide random forest machine learning bioinformatics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • HaiBo Cui
    • 1
  • Jia Wang
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceHubei UniversityWuhanChina
  2. 2.College of ScienceHuazhong Agricultural UniversityWuhanChina

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