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
Citations
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Early prediction of developing spontaneous activity in cultured neuronal networks.
David Cabrera-García,David Cabrera-García,Davide Warm,Davide Warm,Pablo de la Fuente,M. Teresa Fernández-Sánchez,Antonello Novelli,Joaquín Villanueva Balsera +7 more
TL;DR: In this paper, the authors used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro.
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Detection of A and B Influenza Viruses by Surface-Enhanced Raman Scattering Spectroscopy and Machine Learning
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TL;DR: In this article , the authors demonstrate the possibility of applying surface-enhanced Raman spectroscopy (SERS) combined with machine learning technology to detect and differentiate influenza type A and B viruses in a buffer environment.
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A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour in C. elegans
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References
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Saranjam Khan,Rahat Ullah,Asifullah Khan,Ruby Ashraf,Hina Ali,Muhammad Bilal,Muhammad Saleem +6 more
TL;DR: The analysis of hepatitis B virus (HBV) infection in human blood serum using Raman spectroscopy combined with pattern recognition technique and best classification performance has been achieved for polynomial kernel of order-2.
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Medha Bhagwat,L. Aravind +1 more
TL;DR: This chapter discusses practical aspects of using PSI-BLAST and provides a tutorial on how to uncover distant relationships between proteins and use them to reach biologically meaningful conclusions.
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POPISK: T-cell reactivity prediction using support vector machines and string kernels
TL;DR: POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures and relates this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction.
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Practical Raman Spectroscopy: An Introduction
TL;DR: In this article, an open-learning approach to Raman spectroscopy is presented, providing detail on instrumentation, applications and discussions questions throughout the book. But, the focus is on theoretical aspects (e.g. selection rules), but practical aspects are usually disregarded.