Computational Tool for Classification of Dengue Virus

Ali, Ishtiaq; Humayun, Fahad; Azam, Shumaila; Munir, Anum; Rizwan, Muhammad; Mehmood, Azhar; Adnan, Muhammad Abdul Jabar

doi:10.4172/2329-9533.1000140

Cited by 6 publications

(2 citation statements)

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“…Feature selection is the process of selecting the informative approximation wavelet coefficients © 2019 Life Science Informatics Publication All rights reserved Peer review under responsibility of Life Science Informatics Publications 2019 Jan -Feb RJLBPCS 5(1) Page No.369 using chi-squared test value V from (7). Large value of V indicates there exists association between disease and exposure, small value of V indicates no association exists between disease and exposure [16,20].…”

Section: Feature Selectionmentioning

confidence: 99%

Untitled

2019

RJLBPCS

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Dengue is a growing problem in tropical countries. It serves diseases especially in children. Different clinical and computerized methods like ELISA, Plateia, haematocytometer, RT-PCR, decision tree algorithms and artificial neural network are used to diagnose the dengue using blood specimen. These methods are not suitable for identifying the dengue serotypes. To overcome the problem, this paper proposes dengue diagnosis method based on nucleotides in the gene sequence, it needs only skin cells or hair or nail which can be collected easily from the patients. The proposed method not only diagnosing the dengue but also classifies serotypes using wavelet coefficients of EIIP indicator sequences. The experimental results demonstrate that proposed method identifies dengue and its serotypes correctly by EIIP indicator sequence, wavelet coefficients and neural network. Some clinical diagnostic methods take 30 -60 minutes for identifying dengue.The proposed method takes a fraction of seconds for classifying the dengue serotypes. The results of this proposed system can be used for the drug designer for dengue.

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Section: Feature Selectionmentioning

confidence: 99%

Untitled

2019

RJLBPCS

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“…There are a considerable amount of research related to dengue using computational systems [Ali et al 2017, Muthusamy et al 2016. Indeed, a lot of researches involving Machine Learning (ML) to provide differential diagnostic among DF and DHF has used a variety of techniques, such as decision trees [Tanner et al 2008] and Support Vector Machines [Gomes et al 2010].…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence applied to Human Genome Data for the Dengue Severity Prognosis

Davi

Pastor

Oliveira³

et al. 2018

Anais Estendidos Do XI Simpósio Brasileiro De Bioinformática (BSB Estendido 2018)

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Dengue has become one of the most important worldwide arthropodborne diseases around the world. Here, one hundred and two Brazilian dengue virus (DENV) III patients and controls were genotyped for 322 innate immunity gene loci. All biological data (including age, sex and genome background) were analyzed using Machine Learning techniques to discriminate tendency to severe dengue phenotype development. Our current approach produces median values for accuracy greater than 86%, with sensitivity and specificity over 98% and 51%, respectively. Genome data information from 13 key immune polymorphic SNPs was used under different dominant or recessive models. Our approach is a valuable tool for early diagnosis of the severe form of dengue infection and can be used to identify individuals at high risk of developing this form of the disease even in uninfected individuals. The model also identifies various genes involved dengue severity.

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