- Title
- Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks:
- Creator
- Amamuddy, Olivier S, Bishop, Nigel T, Tastan Bishop, Özlem
- 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, pdf
- Language
- English
- Relation
- BMC bioinformatics, 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., 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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Bishop, Nigel T (Prof Emeritus)
RU Department of Mathematics (Pure and Applied)
Tastan Bishop, Ozlem (Prof)
RU Research Unit in Bioinformatics (RUBi)
RU Department of Biochemistry, Microbiology and Bioinformatics
SDG 03. Good Health and Well-Being
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