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
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , 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.
- Full Text:
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , 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.
- Full Text:
MD-TASK: a software suite for analyzing molecular dynamics trajectories
- Brown, David K, Penkler, David L, Amamuddy, Olivier S, Ross, Caroline J, Atilgan, Ali R, Atilgan, Canan, Tastan Bishop, Özlem
- Authors: Brown, David K , Penkler, David L , Amamuddy, Olivier S , Ross, Caroline J , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125138 , vital:35735 , https://doi.10.1093/bioinformatics/btx349
- Description: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.
- Full Text:
- Authors: Brown, David K , Penkler, David L , Amamuddy, Olivier S , Ross, Caroline J , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125138 , vital:35735 , https://doi.10.1093/bioinformatics/btx349
- Description: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.
- Full Text:
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