Application of support vector machines in viral biology
Sonal Modak,Swati Mehta,Deepak Sehgal,Jayaraman Valadi +3 more
- pp 361-403
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TLDR
This chapter provides lucid and easy to understand details of SVM algorithms along with applications in virology, one such robust tool based rigorously on statistical learning theory.Abstract:
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.read more
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References
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Journal Article
Assembly and characterization of pandemic influenza A H1N1 genome in nasopharyngeal swabs using high-throughput pyrosequencing
Barbara Bartolini,Giovanni Chillemi,Isabella Abbate,Alessandro Bruselles,Gabriella Rozera,Tiziana Castrignanò,Daniele Paoletti,Ernesto Picardi,Alessandro Desideri,Graziano Pesole,Maria Rosaria Capobianchi +10 more
TL;DR: De novo high-throughput pyrosequencing was used to detect and characterize 2009 pandemic influenza A (H1N1) virus directly in nasopharyngeal swabs in the context of the microbial community, and could be of great value in identifying possibly new reassortants that may occur in the near future.
Journal ArticleDOI
QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).
Zijian Qin,Maolin Wang,Aixia Yan +2 more
TL;DR: The combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline.
Journal ArticleDOI
Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.
TL;DR: Laboratories looking to include machine learning via SVM as part of their decision support need to be aware that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied.
Journal ArticleDOI
Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests.
TL;DR: This work proposes the application of a hybrid filter-wrapper algorithm employing concepts from the recently developed biogeography based optimization algorithm, in conjunction with SVM and Random Forests for identification of MHC-I binding peptides, and demonstrates the effectiveness of this evolutionary technique, coupled with weighted heuristics, for the construction of improved prediction models.
Journal ArticleDOI
Next-generation sequencing revealed divergence in deletions of the preS region in the HBV genome between different HBV-related liver diseases.
Jian'an Jia,Xiaotao Liang,Shipeng Chen,Hui Wang,Huiming Li,Meng Fang,Xin Bai,Ziyi Wang,Mengmeng Wang,Shanfeng Zhu,Fengzhu Sun,Fengzhu Sun,Chunfang Gao +12 more
TL;DR: A prominent divergence in preS deletion patterns between disease groups and virus genotypes, but not between different tissue types was revealed by the use of the NGS method.