ACPred-FL: a sequence-based predictor based on effective feature representation to improve the prediction of anti-cancer peptides

ACPred-FL: a sequence-based predictor based on effective feature representation to improve the... Abstract Motivation Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides. Results To address this, we propose a novel predictor named ACPred-FL for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using sequence-based feature descriptors. By doing so, the class label information of data samples is fully utilized. To improve the feature representation, we further employ a two-step feature selection technique, resulting in a most informative 5-dimensional feature vector for final peptide representation. Experimental results show that such five features provide the most discriminative power for identifying ACPs than currently available feature descriptors, highlighting the effectiveness of the proposed feature learning approach. The developed ACPred-FL method significantly outperforms state-of-the-art methods. Availability The web-server of ACPred-FL is available at http://server.malab.cn/ACPred-FL. Contact weileyi@tju.edu.cn; jiangning.song@monash.edu; ran.su@tju.edu.cn. Supplementary Information Supplementary data are available at Bioinformatics online. © The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

ACPred-FL: a sequence-based predictor based on effective feature representation to improve the prediction of anti-cancer peptides

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Publisher
Oxford University Press
Copyright
© The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1367-4803
eISSN
1460-2059
D.O.I.
10.1093/bioinformatics/bty451
Publisher site
See Article on Publisher Site

Abstract

Abstract Motivation Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides. Results To address this, we propose a novel predictor named ACPred-FL for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using sequence-based feature descriptors. By doing so, the class label information of data samples is fully utilized. To improve the feature representation, we further employ a two-step feature selection technique, resulting in a most informative 5-dimensional feature vector for final peptide representation. Experimental results show that such five features provide the most discriminative power for identifying ACPs than currently available feature descriptors, highlighting the effectiveness of the proposed feature learning approach. The developed ACPred-FL method significantly outperforms state-of-the-art methods. Availability The web-server of ACPred-FL is available at http://server.malab.cn/ACPred-FL. Contact weileyi@tju.edu.cn; jiangning.song@monash.edu; ran.su@tju.edu.cn. Supplementary Information Supplementary data are available at Bioinformatics online. © The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

BioinformaticsOxford University Press

Published: Jun 1, 2018

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