DocumentCode :
557495
Title :
Artificial neural networks and support vector machine identify Alu elements as being associated with human housekeeping genes
Author :
Dharmasaroja, Permphan
Author_Institution :
Dept. of Anatomy, Mahidol Univ., Bangkok, Thailand
Volume :
3
fYear :
2011
fDate :
15-17 Oct. 2011
Firstpage :
1664
Lastpage :
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.
Keywords :
DNA; bioinformatics; genomics; neural nets; support vector machines; Alu elements; artificial neural network; human housekeeping gene; myocyte specific genes; neuron specific genes; pseudogenes; retrotransposition machinery; short interspersed nuclear repetitive DNA elements; support vector machine; Accuracy; Bioinformatics; DNA; Genomics; Humans; Support vector machines; Training; GEP; MLP; PNN; RBF; SVM; decision tree; genome; interspersed element; myocyte; neuron;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9351-7
Type :
conf
DOI :
10.1109/BMEI.2011.6098522
Filename :
6098522
Link To Document :
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