<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Hepatitis is a global disease that is on the rise and is currently the cause of more deaths than the human immunodeficiency virus each year. As a result, there is an increasing need for antivirals. Previously, effective antivirals have been found in the form of substrate-mimetic antiviral protease inhibitors. The application of machine learning has been used to predict cleavage patterns of viral proteases to provide information for future drug design. This study has successfully applied and compared several machine learning algorithms to hepatitis C viral NS3 serine protease cleavage data. Results have found that differences in sequence-extraction methods can outweigh differences in algorithm choice. Models produced from pseudo-coded datasets all performed with high accuracy and outperformed models created with orthogonal-coded datasets. However, no single pseudo-model performed significantly better than any other. Evaluation of performance measures also show that the correct choice of model scoring system is essential for unbiased model assessment.</p></abstract>

Hepatitis is a global disease that is on the rise and is currently the cause of more deaths than the human immunodeficiency virus each year. As a result, there is an increasing need for antivirals. Previously, effective antivirals have been found in the form of substrate-mimetic antiviral protease inhibitors. The application of machine learning has been used to predict cleavage patterns of viral proteases to provide information for future drug design. This study has successfully applied and compared several machine learning algorithms to hepatitis C viral NS3 serine protease cleavage data. Results have found that differences in sequence-extraction methods can outweigh differences in algorithm choice. Models produced from pseudo-coded datasets all performed with high accuracy and outperformed models created with orthogonal-coded datasets. However, no single pseudo-model performed significantly better than any other. Evaluation of performance measures also show that the correct choice of model scoring system is essential for unbiased model assessment.

A comparison of machine learning algorithms for the prediction of Hepatitis C NS3 protease cleavage sites

The EuroBiotech Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hepatitis is a global disease that is on the rise and is currently the cause of more deaths than the human immunodeficiency virus each year. As a result, there...

A comparison of machine learning algorithms for the prediction of Hepatitis C NS3 protease cleavage sites

Publié en ligne: 23 oct. 2019

Pages: 167 - 174

DOI: https://doi.org/10.2478/ebtj-2019-0020

Mots clés
Hepatitis C, NS3 protease, peptide cleavage, machine learning

© 2019 Harry Chown, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A comparison of machine learning algorithms for the prediction of Hepatitis C NS3 protease cleavage sites

Publié en ligne: 23 oct. 2019

Pages: 167 - 174

DOI: https://doi.org/10.2478/ebtj-2019-0020

Mots clésHepatitis C, NS3 protease, peptide cleavage, machine learning

© 2019 Harry Chown, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Hepatitis C, NS3 protease, peptide cleavage, machine learning