Support vector machine for 3D modelling from sparse geological information of various origins
Computers & Geosciences
Recognition of DNase I hypersensitive sites in multiple cell lines
International Journal of Bioinformatics Research and Applications
Proceedings of the 12th annual conference on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Detecting RNA sequences using two-stage SVM classifier
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
The gapped spectrum kernel for support vector machines
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Fast classification for large data sets via random selection clustering and Support Vector Machines
Intelligent Data Analysis
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Motivation: In the living cell nucleus, genomic DNA is packaged into chromatin. DNA sequences that regulate transcription and other chromosomal processes are associated with local disruptions, or 'openings', in chromatin structure caused by the cooperative action of regulatory proteins. Such perturbations are extremely specific for cis-regulatory elements and occur over short stretches of DNA (typically ∼250 bp). They can be detected experimentally as DNaseI hypersensitive sites (HSs) in vivo, though the process is extremely laborious and costly. The ability to discriminate DNaseI HSs computationally would have a major impact on the annotation and utilization of the human genome. Results: We found that a supervised pattern recognition algorithm, trained using a set of 280 DNaseI HS and 737 non-HS control sequences from erythroid cells, was capable of de novo prediction of HSs across the human genome with surprisingly high accuracy determined by prospective in vivo validation. Systematic application of this computational approach will greatly facilitate the discovery and analysis of functional non-coding elements in the human and other complex genomes. Availability: Supplementary data is available at noble.gs.washington.edu/proj/hs Contact:noble@gs.washington.edu; jstam@regulome.com