- 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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