About: Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses

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

About: Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses 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	Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses
Creator	Adebiyi, Marion Adetiba, Emmanuel Akanle, Matthew Badejo, Joke Matthews, Victor »more»
source	PMC
abstract	Accurate detection of pathogenic viruses has become highly imperative. This is because viral diseases constitute a huge threat to human health and wellbeing on a global scale. However, both traditional and recent techniques for viral detection suffer from various setbacks. In codicil, some of the existing alignment-free methods are also limited with respect to viral detection accuracy. In this paper, we present the development of an alignment-free, digital signal processing based method for pathogenic viral detection named Z-Curve Genomic Cesptral Coefficients (ZCGCC). To evaluate the method, ZCGCC were computed from twenty six pathogenic viral strains extracted from the ViPR corpus. Naïve Bayesian classifier, which is a popular machine learning method was experimentally trained and validated using the extracted ZCGCC and other alignment-free methods in the literature. Comparative results show that the proposed ZCGCC gives good accuracy (93.0385%) and improved performance to existing alignment-free methods.
has issue date	2018-03-07(xsd:dateTime)
bibo:doi	10.1007/978-3-319-78723-7_25
has license	no-cc
sha1sum (hex)	1cd926ca105e65830de6fbcc99914c050ed14d94
schema:url	https://doi.org/10.1007/978-3-319-78723-7_25
resource representing a document's title	Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses
has PubMed Central identifier	PMC7120486
schema:publication	Bioinformatics and Biomedical Engineering
resource representing a document's body	covid:1cd926ca105e65830de6fbcc99914c050ed14d94#body_text
is schema:about of	named entity 'CURVE' named entity 'existing' named entity 'traditional' named entity 'Comparative' named entity 'detection' »more»

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