Meta-reg: A Computational Metaheuristic Framework to Improve SVM-Based Prediction of Regulatory Activity

Part of the IFMBE Proceedings book series (IFMBE, volume 49)


Gene regulatory activity prediction is an important step to understand which Transcription Factors (TFs) are important for regulation of gene expression in cells. The development of recent high throughput technologies and machine learning approaches allow us to archive this task more efficiently. Support Vector Machine (SVM) has been successfully applied for the case of predicting gene regulatory activity in Drosophila embryonic development. Here, we introduce meta-heuristic approaches to select the best parameters for regulatory prediction from transcription factor binding profiles. Experimental results show that our approach outperforms existing methods and the potentials for further analysis beyond the prediction.


Gene regulation machine learning support vector machine ant colony optimization 


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Copyright information

© IFMBE 2013

Authors and Affiliations

  1. 1.Institute of Information TechnologyVietnam National UniversityHanoiVietnam
  2. 2.College of TechnologyVietnam National University of HanoiHanoiVietnam
  3. 3.Center for Integrative BioinformaticsMax F. Perutz LaboratoriesViennaAustria
  4. 4.Gregor Mendel Institute of Molecular Plant BiologyViennaAustria

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