article

Intelligent consensus modeling for proline cis-trans isomerization prediction

Editor: Ying Xu Authors:

Jamal Bentahar,

Abdallah ShamiAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 11, Issue 1

Pages 26 - 32

https://doi.org/10.1109/TCBB.2013.132

Published: 01 January 2014 Publication History

Abstract

Proline cis-trans isomerization (CTI) plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline CTI predictor based on a biophysically motivated intelligent consensus modeling through the use of sequence information only (i.e., position specific scores generated by PSI-BLAST). The current computational proline CTI predictors reach about 70-73 percent Q2 accuracies and about 0.40 Matthew correlation coefficient (Mcc) through the use of sequence-based evolutionary information as well as predicted protein secondary structure information. However, our approach that utilizes a novel decision tree-based consensus model with a powerful randomized-metalearning technique has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on the same proline CTI data set, which is a better result than those of any existing computational proline CTI predictors reported in the literature.

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Cited By

Al-Jarrah OYoo PTaha KMuhaidat SShami AZaki N(2015)Randomized subspace learning for proline CIS-trans isomerization predictionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2014.236904012:4(763-769)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1109/TCBB.2014.2369040
Diamantoulakis PKapinas VKaragiannidis G(2015)Big Data Analytics for Dynamic Energy Management in Smart GridsBig Data Research10.1016/j.bdr.2015.03.0032:3(94-101)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.bdr.2015.03.003

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Intelligent consensus modeling for proline cis-trans isomerization prediction

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Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 11, Issue 1

January/February 2014

265 pages

ISSN:1545-5963

Editor:
Ying Xu

Issue’s Table of Contents

Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 January 2014

Accepted: 14 October 2013

Received: 09 October 2013

Published in TCBB Volume 11, Issue 1

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Cited By

Al-Jarrah OYoo PTaha KMuhaidat SShami AZaki N(2015)Randomized subspace learning for proline CIS-trans isomerization predictionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2014.236904012:4(763-769)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1109/TCBB.2014.2369040
Diamantoulakis PKapinas VKaragiannidis G(2015)Big Data Analytics for Dynamic Energy Management in Smart GridsBig Data Research10.1016/j.bdr.2015.03.0032:3(94-101)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.bdr.2015.03.003

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