ArticlePloS one2014
A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.BMC bioinformatics · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning.BMC genomics · 2023Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping.Sensors (Basel, Switzerland) · 2022Article
- Web tools to perform long non-coding RNAs analysis in oncology research.Database : the journal of biological databases and curation · 2021Review
- Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.Biomedical engineering online · 2021Article
- Article
- MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models.International journal of molecular sciences · 2019Article
- Imbalance learning for the prediction of NBMC genomics · 2018Article
- ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons.Journal of chemical information and modeling · 2018Article
- Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins.BMC bioinformatics · 2016Article
- Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.The Journal of membrane biology · 2016Article
- A hybrid random forest model for stroke risk prediction.Frontiers in neurologyArticle
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Authors and funding
6 authors.
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Abstract
Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imbalanced learning problem, where binding residues are extremely fewer in number than non-binding residues. Alleviating the severity of class imbalance has been demonstrated to be a promising means of improving the prediction performance of a machine-learning-based predictor for class imbalance problems. However, little attention has been paid to the negative impact of class imbalance on protein-nucleotide binding residue prediction. In this study, we propose a new supervised over-sampling algorithm that synthesizes additional minority class samples to address class imbalance. The experimental results from protein-nucleotide interaction datasets demonstrate that the proposed supervised over-sampling algorithm can relieve the severity of class imbalance and help to improve prediction performance. Based on the proposed over-sampling algorithm, a predictor, called TargetSOS, is implemented for protein-nucleotide binding residue prediction. Cross-validation tests and independent validation tests demonstrate the effectiveness of TargetSOS. The web-server and datasets used in this study are freely available at http://www.csbio.sjtu.edu.cn/bioinf/TargetSOS/.
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