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

Journal: BMC bioinformatics

PMID: 28810826

Abstract

BACKGROUND: 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.

Authors

Olivier Sheik Amamuddy

Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa.
Nigel T Bishop

Department of Mathematics (Pure and Applied), Rhodes University, Grahamstown, 6140, South Africa.
Özlem Tastan Bishop

Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.

Keywords

Databases, Factual Drug Resistance, Viral HIV Infections HIV Protease HIV Protease Inhibitors HIV Reverse Transcriptase HIV-1 Humans Neural Networks, Computer Reverse Transcriptase Inhibitors

External Resources

View on PubMed Access via DOI PubMed (28810826)

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

Abstract

Authors

Keywords

External Resources

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