ArticleMolecules (Basel, Switzerland)2017
EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 26 citations in OpenAlex.
- Charting γ-secretase substrates by explainable AI.Nature communications · 2025Article
- Electrostatic interactions guide substrate recognition of the prokaryotic ubiquitin-like protein ligase PafA.Nature communications · 2023Article
- Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification.Molecules (Basel, Switzerland) · 2023Article
- CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning.Expert systems with applications · 2021Article
- PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations.International journal of molecular sciences · 2021Article
- PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids.Genes · 2020Article
- Positive-unlabelled learning of glycosylation sites in the human proteome.BMC bioinformatics · 2019Article
- Recognition of Protein Pupylation Sites by Adopting Resampling Approach.Molecules (Basel, Switzerland) · 2018Article
- EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites.Molecules (Basel, Switzerland) · 2017Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
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.
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