ArticlePloS one2012
Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Improving random forest predictions in small datasets from two-phase sampling designs.BMC medical informatics and decision making · 2021Trial
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.Biomolecules · 2025Review
- Computational Identification of Milk Trait Regulation Through Transcription Factor Cooperation in Murciano-Granadina Goats.Biology · 2024Article
- SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.Communications biology · 2024Article
- An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction.International journal of molecular sciences · 2024Article
- Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.Genes · 2024Article
- MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction.Frontiers in genetics · 2024Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.Frontiers in microbiology · 2023Article
- Article
- Multi-task learning to leverage partially annotated data for PPI interface prediction.Scientific reports · 2022Article
- Deciphering Pleiotropic Signatures of Regulatory SNPs inInternational journal of molecular sciences · 2022Article
- Ensemble machine learning model identifies patients with HFpEF from matrix-related plasma biomarkers.American journal of physiology. Heart and circulatory physiology · 2022Article
- Predicting protein-protein interactions between banana and Fusarium oxysporum f. sp. cubense race 4 integrating sequence and domain homologous alignment and neural network verification.Proteome science · 2022Article
- ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.Briefings in bioinformatics · 2022Article
- Effective Macrosomia Prediction Using Random Forest Algorithm.International journal of environmental research and public health · 2022Article
- RF_phage virion: Classification of phage virion proteins with a random forest model.Frontiers in genetics · 2022Article
- Deep learning frameworks for protein-protein interaction prediction.Computational and structural biotechnology journal · 2022Review
- DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction.
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