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Efficient Mode of Action Identification by Support Vector Machine Regression

  • Vitoantonio Bevilacqua
  • Paolo Pannarale
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. Our approach has the advantage of not needing control samples, libraries or numerous assays. The so far proposed implementations of this strategy are computationally demanding. Our solution, while performing comparably to state of the art algorithms in terms of discovered targets, is more efficient in terms of memory and time consumption.

Keywords

Action Identification Support Vector Machine Regression Correlation Base Feature Selection Thetic Process Gene Ontology Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vitoantonio Bevilacqua
    • 1
    • 2
  • Paolo Pannarale
    • 1
  1. 1.Dipartimento di Elettrotecnica ed ElettronicaPolitecnico di BariBariItaly
  2. 2.eBisBariItaly

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