• 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