Application of support vector machines in viral biology
Sonal Modak,Swati Mehta,Deepak Sehgal,Jayaraman Valadi +3 more
- pp 361-403
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
Citations
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Early prediction of developing spontaneous activity in cultured neuronal networks.
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References
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TL;DR: The aim of this study was to develop and demonstrate an approach for describing the diversity of human pathogenic viruses in an environmentally isolated viral metagenome.
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Prediction of protein-protein interactions between viruses and human by an SVM model
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SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
Lawrence J K Wee,Lawrence J K Wee,Diane Simarmata,Yiu-Wing Kam,Lisa F. P. Ng,Lisa F. P. Ng,Joo Chuan Tong,Joo Chuan Tong +7 more
TL;DR: A support vector machines (SVM) prediction model utilizing Bayes Feature Extraction was developed and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins.
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DeNovo: virus-host sequence-based protein-protein interaction prediction.
TL;DR: DeNovo is a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins, and achieves near optimal accuracy when tested on bacteria-human interactions.
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SVMHC: a server for prediction of MHC-binding peptides.
Pierre Dönnes,Oliver Kohlbacher +1 more
TL;DR: The SVMHC server for prediction of both MHC class I and class II binding peptides is presented and offers fast analysis of a wide range of alleles and prediction results are given in several comprehensive formats.