King Mongkut's University of Technology Reports Findings in Algorithms (Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm)
By a News Reporter-Staff News Editor at Life Science Weekly -- Research findings on Algorithms are discussed in a new report. According to news reporting out of Bangkok, Thailand, by NewsRx editors, research stated, "To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively."
Our news journalists obtained a quote from the research from the King Mongkut's University of Technology, "The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features-structure, sequence, modularity, structural robustness and coding potential-to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively."
According to the news editors, the research concluded: "Our classifier is available at http://ncrna-pred.com/HLRF.htm."
For more information on this research see: Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm. Nucleic Acids Research, 2014;42(11):50-61. Nucleic Acids Research can be contacted at: Oxford Univ Press, Great Clarendon St, Oxford OX2 6DP, England. (Oxford University Press - www.oup.com/; Nucleic Acids Research - nar.oxfordjournals.org)
Our news journalists report that additional information may be obtained by contacting S. Lertampaiporn, King Mongkuts University of Technology, Bioinformat & Syst Biol Program, Bangkok 10150, Thailand. Additional authors for this research include C. Thammarongtham, C. Nukoolkit, B. Kaewkamnerdpong and M. Ruengjitchatchawalya (see also Algorithms).
Keywords for this news article include: Asia, Bangkok, Thailand, Algorithms
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