Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12623
DC FieldValueLanguage
dc.contributor.authorChrysostomou, Charalambos-
dc.contributor.authorPartaourides, Harris-
dc.contributor.authorSeker, Huseyin-
dc.date.accessioned2018-08-08T06:35:07Z-
dc.date.available2018-08-08T06:35:07Z-
dc.date.issued2017-09-13-
dc.identifier.citation39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2017, Seogwipo, South Korea, 11-15 Julyen_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/12623-
dc.description.abstractThe Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested. The predictive model follows three main steps; (i) decoding the protein sequences into numerical signals using EIIP amino acid scale, (ii) analysing these sequences by using Discrete Fourier Transform (DFT) and extracting DFT-based features, (iii) using a predictive model, based on Artificial Neural Networks and using the features generated by DFT. In this analysis, from the Influenza Research Database, 30724, 18236 and 8157 HA protein sequences were collected for Human, Avian and Swine respectively. Given this set of the proteins, the proposed method yielded 97.36% (± 0.04%), 97.26% (± 0.26%), 0.978 (± 0.004), 0.963 (± 0.005) and 0.945 (±0.005) for the training accuracy validation accuracy, precision, recall and Mathews Correlation Coefficient (MCC) respectively, based on a 10-fold cross-validation. The classification model generated by using one of the largest dataset, if not the largest, yields promising results that could lead to early detection of such species and help develop precautionary measurements for possible human infections.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2017 IEEE.en_US
dc.subjectArtificial Neural Networken_US
dc.subjectAmino Acid Indicesen_US
dc.subjectDiscrete Fourier Transform (DFT)en_US
dc.subjectHemagglutinin (HA) Proteinen_US
dc.titlePrediction of influenza A virus infections in humans using an Artificial Neural Network learning approachen_US
dc.typeConference Papersen_US
dc.doihttps://doi.org/10.1109/EMBC.2017.8037042en_US
dc.collaborationThe Cyprus Instituteen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationThe University of Northumbria at Newcastleen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
cut.common.academicyear2017-2018en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-8555-260X-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
CORE Recommender
Show simple item record

Page view(s) 50

401
Last Week
0
Last month
28
checked on Mar 14, 2025

Google ScholarTM

Check


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.