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

Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks.
Author Information (click to view)

Sheik Amamuddy O, Bishop NT, Tastan Bishop Ö,


Sheik Amamuddy O, Bishop NT, Tastan Bishop Ö, (click to view)

Sheik Amamuddy O, Bishop NT, Tastan Bishop Ö,

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BMC bioinformatics 2017 08 1518(1) 369 doi 10.1186/s12859-017-1782-x

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.

RESULTS
Trained regression ANNs were optimized for eight protease inhibitors, six nucleoside reverse transcriptase (RT) inhibitors and four non-nucleoside RT inhibitors by experimenting combinations of rare variant filtering (none versus 1 residue occurrence) and ANN topologies (1-3 hidden layers with 2, 4, 6, 8 and 10 nodes per layer). Single hidden layers (5-20 nodes) were used for training where overfitting was detected. 5-fold cross-validation produced mean R(2) values over 0.95 and standard deviations lower than 0.04 for all but two antiretrovirals.

CONCLUSIONS
Overall, higher accuracies and lower variances (compared to results published in 2016) were obtained by experimenting with various preprocessing methods, while focusing on the most prevalent subtype in the raw dataset (subtype B).We thus highlight the need to develop and make available subtype-specific datasets for developing higher accuracy in drug-resistance prediction methods.

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