EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites
AbstractProtein 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://18.104.22.168:8080/EPuL View Full-Text
- Supplementary File 1:
Supplementary (PDF, 298 KB)
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
Nan X, Bao L, Zhao X, Zhao X, Sangaiah AK, Wang G-G, Ma Z. EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites. Molecules. 2017; 22(9):1463.Chicago/Turabian Style
Nan, Xuanguo; Bao, Lingling; Zhao, Xiaosa; Zhao, Xiaowei; Sangaiah, Arun K.; Wang, Gai-Ge; Ma, Zhiqiang. 2017. "EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites." Molecules 22, no. 9: 1463.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.