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Informative transcription factor selection using support vector machine-based generalized approximate cross validation criteria

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  • Sohn, Insuk
  • Shim, Jooyong
  • Hwang, Changha
  • Kim, Sujong
  • Lee, Jae Won

Abstract

The genetic regulatory mechanism plays a pivotal role in many biological processes ranging from development to survival. The identification of the common transcription factor binding sites (TFBSs) from a set of known co-regulated gene promoters and the identification of genes that are regulated by the transcription factor (TF) that have important roles in a particular biological function will advance our understanding of the interaction among the co-regulated genes and intricate genetic regulatory mechanism underlying this function. To identify the common TFBSs from a set of known co-regulated gene promoters and classify genes that are regulated by TFs, the new approaches using Support Vector Machine (SVM)-based Generalized Approximate Cross Validation (GACV) criteria are proposed. Two variable selection methods are considered for Recursive Feature Elimination (RFE) and Recursive Feature Addition (RFA). Performances of the proposed methods are compared with the existing SVM-based criteria, Logistic Regression Analysis (LRA), Logic Regression (LR), and Decision Tree (DT) methods by using both two real TF target genes data and the simulated data. In terms of test error rates, the proposed methods perform better than the existing methods.

Suggested Citation

  • Sohn, Insuk & Shim, Jooyong & Hwang, Changha & Kim, Sujong & Lee, Jae Won, 2009. "Informative transcription factor selection using support vector machine-based generalized approximate cross validation criteria," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1727-1735, March.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1727-1735
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    References listed on IDEAS

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    1. Sunduz Keles & Mark van der Laan & Chris Vulpe, 2004. "Regulatory Motif Finding by Logic Regression," U.C. Berkeley Division of Biostatistics Working Paper Series 1145, Berkeley Electronic Press.
    2. Yoonkyung Lee & Yuwon Kim & Sangjun Lee & Ja-Yong Koo, 2006. "Structured multicategory support vector machines with analysis of variance decomposition," Biometrika, Biometrika Trust, vol. 93(3), pages 555-571, September.
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    1. Insuk Sohn & Jooyong Shim & Changha Hwang & Sujong Kim & Jae Won Lee, 2014. "Transcription factor-binding site identification and gene classification via fusion of the supervised-weighted discrete kernel clustering and support vector machine," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 573-581, March.

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