About: Application of Support Vector Machines in Viral Biology

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Application of Support Vector Machines in Viral Biology Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Application of Support Vector Machines in Viral Biology
Creator	Sehgal, Deepak Mehta, Swati Mehta, · Modak, Sonal Modak, S Sehgal, · Valadi, J Valadi, Jayaraman
source	PMC
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.
has issue date	2019-11-23(xsd:dateTime)
bibo:doi	10.1007/978-3-030-29022-1_12
has license	no-cc
sha1sum (hex)	08b36a81cd902cd4794619fb24ca101da11bfadb
schema:url	https://doi.org/10.1007/978-3-030-29022-1_12
resource representing a document's title	Application of Support Vector Machines in Viral Biology
has PubMed Central identifier	PMC7114997
schema:publication	Global Virology III: Virology in the 21st Century
resource representing a document's body	covid:08b36a81cd902cd4794619fb24ca101da11bfadb#body_text
is schema:about of	named entity 'LEARNING THEORY' named entity 'INVOLVING' named entity 'INTERDISCIPLINARY' named entity 'DIFFERENT' named entity 'MACHINE LEARNING' named entity 'IDENTIFICATION' named entity 'VIROLOGY' named entity 'TOOL' named entity 'ACCURACY' named entity 'GAIN' named entity 'IMPORTANT' named entity 'KNOWLEDGE' named entity 'VERY HIGH' named entity 'INCREASING' named entity 'TYPES' named entity 'ALGORITHMS' named entity 'SVM' named entity 'ROBUST' named entity 'UNDERSTAND' named entity 'TOOLS' named entity 'PROTEIN ' named entity 'SOLUTIONS' named entity 'NECESSARY' named entity 'APPLICATIONS' named entity 'BIOLOGY' named entity 'PROVIDE' named entity 'VIRAL GENOMICS' named entity 'APPROACHES' named entity 'EMPLOY' named entity 'PROVIDING' named entity 'REGRESSION' named entity 'VIRAL' named entity 'SUPPORT VECTOR MACHINES' named entity 'TRANSFORM' named entity 'NOVEL' named entity 'CLASSIFICATION' named entity 'FUNCTIONS' named entity 'LUCID' named entity 'APPLICATION' named entity 'STUDIES' named entity 'DATA' named entity 'CHAPTER' named entity 'EXPLOSION' named entity 'GENE' named entity 'SUPPORT VECTOR MACHINES' named entity 'RAPIDLY' named entity 'INFORMATION' named entity 'PROVIDES' named entity 'HAVE' named entity 'BENEFICIAL' named entity 'MODELS' named entity 'BASED' named entity 'STRONG' named entity 'EXPERTISE' named entity 'PARADIGMS' named entity 'PERFORMANCE' named entity 'EASY' named entity 'SEQUENCING TECHNIQUES' named entity 'HIGH' named entity 'DUE TO' named entity 'INCLUDING' named entity 'DEVELOPMENT' named entity 'LED' named entity 'DETAILS' named entity 'PROBLEMS' named entity 'EXPONENTIAL' named entity 'EXPERIMENTAL' named entity 'QUALITY' named entity 'BASES' covid:arg/08b36a81cd902cd4794619fb24ca101da11bfadb named entity 'protein' named entity 'Support Vector Machines' named entity 'SVM' named entity 'virology' named entity 'interdisciplinary' named entity 'Support Vector Machines'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software