Protein fold classification with Grow-and-Learn network

Özlem POLAT; Zümray DOKUR

Article Details

Protein fold classification with Grow-and-Learn network

Authors:

Özlem POLAT | Zümray DOKUR

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 2, pp.1184-1196

Document Type:

Article

Publication Date:

3/2017

Document Language:

Turkish

Document ID (Org No.):

2000741075

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

Protein fold classification is an important subject in   computational biology and a compelling work from the point of machine   learning. To deal with such a challenging problem, in this study, we propose   a solution method for the classification of protein folds using Grow-and-Learn (GAL)   neural network together with one-versus-others (OvO) method. To   classify the most common 27 protein folds, 125 dimensional data, constituted   by the physicochemical properties of amino acids, are used. The study is   conducted on a database including 694 proteins: 311 of these proteins are   used for training and 383 of them for testing. Overall, the classification   system achieves 81.2% fold recognition accuracy on the test set, where   most of the proteins have less than 25% sequence identity with the ones   used during the training. To portray the capabilities of the GAL network   among the other methods, comparisons between a few approaches have also been   made, and GAL's accuracy is found to be higher than those of the existing   methods for protein fold classification.

Article Details

Protein fold classification with Grow-and-Learn network

Authors: Özlem POLAT | Zümray DOKUR

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 2, pp.1184-1196

Document Type: Article

Publication Date: 3/2017

Document Language: Turkish

Document ID (Org No.): 2000741075

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Özlem POLAT | Zümray DOKUR

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 2, pp.1184-1196

Document Type:

Article

Publication Date:

3/2017

Document Language:

Turkish

Document ID (Org No.):

2000741075

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632