Please use this identifier to cite or link to this item: https://ir.sc.mahidol.ac.th/handle/123456789/716
Title: Artificial neural networks and support vector machine identify Alu elements as being associated with human housekeeping genes
Authors: Permphan Dharmasaroja
Keywords: decision tree;genome;GEP;interspersed element;MLP;myocyte;neuron;PNN;RBF;SVM
Issue Date: 2011
Publisher: 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011
Citation: กายวิภาคศาสตร์
Series/Report no.: ;1664-1668
Abstract: The human genome contains the most common 75S-and tRNA-derived short interspersed nuclear repetitive DNA elements (SINEs), named Alu. Alu elements, other SINEs, and processed pseudogenes are all processed by the same retrotransposition machinery. Most housekeeping genes contain multiple copies of processed pseudogenes. The present study showed that mean percentage of SINEs in the sequences of housekeeping genes was significantly higher than that of neuron-(p < 0.001) and myocyte-specific genes (p < 0.01). Consistently, GEP, RBF, MLP, PNN, and SVM showed that SINEs were the most important factor associated with housekeeping genes, with the value > 19.54% being most predictive. Based on the area under the receiver operating characteristic curves, there was no significant difference among these classifiers. Detailed analysis of the components of SINEs showed that housekeeping genes contained more Alus than neuron- and myocyte-specific genes (p < 0.001), which were supported by all neural networks and SVM.
Description: Scopus
URI: https://ir.sc.mahidol.ac.th/handle/123456789/716
ISSN: 978-142449352-4
Appears in Collections:Anatomy: International Proceedings

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