Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks:

Amamuddy, Olivier S; Bishop, Nigel T; Tastan Bishop, Özlem

Title: Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks:
Creator: Amamuddy, Olivier S
Creator: Bishop, Nigel T
Creator: Tastan Bishop, Özlem
Date Issued: 2017
Date: 2017
Type: text
Type: article
Identifier: http://hdl.handle.net/10962/148261
Identifier: vital:38724
Identifier: https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
Format: 8 pages
Format: pdf
Language: English
Relation: BMC bioinformatics
Relation: Amamuddy, O.S., Bishop, N.T. and Bishop, Ö.T., 2017. Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks. BMC bioinformatics, 18(1), p.369.
Relation: BMC bioinformatics volume 18 number 1 p.369 August 2017 1471-2105
Rights: Publisher
Rights: Use of this resource is governed by the terms and conditions of the Creative Commons Attribution 4.0 International License (https://0-bmcbioinformatics.biomedcentral.com.wam.seals.ac.za/articles/10.1186/s12859-017-1782-x#citeas)

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Tastan Bishop, Ozlem (Prof)

RU Research Unit in Bioinformatics (RUBi)

RU Department of Biochemistry, Microbiology and Biotechnology

RU Department of Mathematics (Pure and Applied)

Bishop, Nigel T (Prof Emeritus)

SDG 03. Good Health and Well-Being

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