Application of Support Vector Machines in Viral Biology

  • Sonal Modak
  • Swati Mehta
  • Deepak Sehgal
  • Jayaraman ValadiEmail author


Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.


Support vector machines Supervised learning Classification Regression function identification Epitope prediction Quantitative structure activity relationships Domain attributes Attribute selection viral biology 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sonal Modak
    • 1
  • Swati Mehta
    • 1
  • Deepak Sehgal
    • 1
  • Jayaraman Valadi
    • 1
    • 2
    Email author
  1. 1.Life Sciences and Healthcare UnitPersistent Systems Ltd.PuneIndia
  2. 2.Center for Development of Advanced Computing, Savitri Bai Phule Pune UniversityPuneIndia

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