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Molecules 2017, 22(9), 1463; doi:10.3390/molecules22091463

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites

1
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
2
School of Computing Science and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
3
School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
Received: 23 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 5 September 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Protein pupylation is a type of post-translation modification, which plays a crucial role in cellular function of bacterial organisms in prokaryotes. To have a better insight of the mechanisms underlying pupylation an initial, but important, step is to identify pupylation sites. To date, several computational methods have been established for the prediction of pupylation sites which usually artificially design the negative samples using the verified pupylation proteins to train the classifiers. However, if this process is not properly done it can affect the performance of the final predictor dramatically. In this work, different from previous computational methods, we proposed an enhanced positive-unlabeled learning algorithm (EPuL) to the pupylation site prediction problem, which uses only positive and unlabeled samples. Firstly, we separate the training dataset into the positive dataset and the unlabeled dataset which contains the remaining non-annotated lysine residues. Then, the EPuL algorithm is utilized to select the reliably negative initial dataset and then iteratively pick out the non-pupylation sites. The performance of the proposed method was measured with an accuracy of 90.24%, an Area Under Curve (AUC) of 0.93 and an MCC of 0.81 by 10-fold cross-validation. A user-friendly web server for predicting pupylation sites was developed and was freely available at http://59.73.198.144:8080/EPuL View Full-Text
Keywords: positive-unlabeled learning algorithm; pupylation sites; prediction; web server; support vector machine positive-unlabeled learning algorithm; pupylation sites; prediction; web server; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Nan, X.; Bao, L.; Zhao, X.; Zhao, X.; Sangaiah, A.K.; Wang, G.-G.; Ma, Z. EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites. Molecules 2017, 22, 1463.

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