C4.5: programs for machine learning
C4.5: programs for machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Markov Encoding for Detecting Signals in Genomic Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bioinformatics
Bioinformatics
Type-2 fuzzy Gaussian mixture models
Pattern Recognition
Symmetries from uniform space covering in stochastic discrimination
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Type-2 fuzzy hidden Markov models and their application to speech recognition
IEEE Transactions on Fuzzy Systems
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Comment on "SCS: Signal, Context, and Structure Features for Genome-Wide Human Promoter Recognition”
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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This paper integrates the signal, context, and structure features for genome-wide human promoter recognition, which is important in improving genome annotation and analyzing transcriptional regulation without experimental supports of ESTs, cDNAs, or mRNAs. First, CpG islands are salient biological signals associated with approximately 50 percent of mammalian promoters. Second, the genomic context of promoters may have biological significance, which is based on n-mers (sequences of n bases long) and their statistics estimated from training samples. Third, sequence-dependent DNA flexibility originates from DNA 3D structures and plays an important role in guiding transcription factors to the target site in promoters. Employing decision trees, we combine above signal, context, and structure features to build a hierarchical promoter recognition system called SCS. Experimental results on controlled data sets and the entire human genome demonstrate that SCS is significantly superior in terms of sensitivity and specificity as compared to other state-of-the-art methods. The SCS promoter recognition system is available online as supplemental materials for academic use and can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.95.