Instance-Based Learning Algorithms
Machine Learning
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Identifying Simple Discriminatory Gene Vectors with an Information Theory Approach
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Data mining in bioinformatics using Weka
Bioinformatics
A Mathematical Theory of Communication
A Mathematical Theory of Communication
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
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MicroRNAs (miRNAs) have been shown to play important roles in post-transcriptional gene regulation. The hairpin structure is a key characteristic of the microRNAs precursors (pre-miRNAs). How to encode their hairpin structures is a critical step to correctly detect the pre-miRNAs from background sequences, i.e., pseudo miRNA precursors. In this paper, we have proposed to encode the hairpin structures of the pre-miRNA with a set of features, which captures both the global and local structure characteristics of the pre-miRNAs. Furthermore, we find that four essential attributes are discriminatory for classifying human pre-miRNAs and background sequences with an information theory approach. The experimental results show that the number of conserved essential attributes decreases when the phylogenetic distance between the species increases. Specifically, one A-U pair, which produces the U at the start position of most mature miRNAs, in the pre-miRNAs is found to be well conserved in different species for the purpose of biogenesis.