Publication: Artificial neural networks and support vector machine identify Alu elements as being associated with human housekeeping genes
1
Issued Date
2011-12-01
Resource Type
Other identifier(s)
2-s2.0-84855820723
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011. Vol.3, (2011), 1664-1668
Suggested Citation
Permphan Dharmasaroja Artificial neural networks and support vector machine identify Alu elements as being associated with human housekeeping genes. Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011. Vol.3, (2011), 1664-1668. doi:10.1109/BMEI.2011.6098522 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/11870
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Artificial neural networks and support vector machine identify Alu elements as being associated with human housekeeping genes
Author(s)
Other Contributor(s)
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. © 2011 IEEE.