RESEARCH ARTICLE


Identification of Better Gene Expression Data for Mosquito Species Classification Using Radial Basis Function Network Methodology



J. Satya Eswari1, *, Ch. Venkateswarlu2
1 Deparment of Biotechnology, National Institute of Technology Raipur, Raipur, India
2 Department of Chemical Engineering , BV Raju Institute of Technology, Narsapur 502313, India


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 1028
Abstract HTML Views: 756
PDF Downloads: 259
ePub Downloads: 247
Total Views/Downloads: 2290
Unique Statistics:

Full-Text HTML Views: 459
Abstract HTML Views: 328
PDF Downloads: 187
ePub Downloads: 175
Total Views/Downloads: 1149



© 2018 Eswari and Venkateswarlu.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Deparment of Biotechnology, National Institute of Technology Raipur, Raipur, India; Tel: +918919640943; E-mail: satyaeswarij.bt@nitrr.ac.in


Abstract

Background:

Investigation in bioinformatics has developed promptly in latest years owing to improvements in sequence excavating techniques. Gene sequences in DNA are supplemented with great extent of information, but the intricacy and complexity of this information causes difficulty in analyzing it by using standard classical methods of classification. In this work, a Radial Basis Function Network (RBFN) methodology with self-network arrangement is presented for identification of mosquito species based on the genetic design content of ITS2 ribosomal DNA sequences.

Methods:

A number of data sequences in varying sizes of different vectors corresponding to Anopheline, Aedes and Culex genera are used to develop genera specific as well as comprehensive RBFN species identifiers. The recall and generalization ability of the proposed species identifiers are analyzed and further validated through bootstrap validation method. The genera specific RBFN identifiers are found to provide accurate identification of mosquito species of individual genera. However, the comprehensive RBFN model is found to exhibit better species identification ability and can be advantageously used for species identification of more mosquito genera.

Results & Conclusion:

The results demonstrate the usefulness of the RBFN methodology for accurate identification of mosquito species depending on the nucleotide data of ITS2 ribosomal DNA sequences.

Keywords: Mosquitoes, ITS2 Sequences, Modeling, Radial Basis Function Networks, Species Classification, Gene sequence.