ArticleMolecules (Basel, Switzerland)2016
iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.
Article in Molecules (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed.
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
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- Implantable Electrochemical Sensors for Brain Research.JACS Au · 2023Review
- ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with PseudoInternational journal of molecular sciences · 2022Article
- A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.Frontiers in genetics · 2021Article
- Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.Molecular genetics and genomics : MGG · 2020Article
- Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.Genes · 2020Article
- Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.IEEE access : practical innovations, open solutions · 2020Article
- Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information.Evolutionary bioinformatics online · 2020Article
- Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.BMC molecular and cell biology · 2019Article
- SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences.Bioinformatics (Oxford, England) · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction.BMC genomics · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.Scientific reports · 2018Article
- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 2018Article
- A proteome-wide systems toxicological approach deciphers the interaction network of chemotherapeutic drugs in the cardiovascular milieu.RSC advances · 2018Article
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5 authors.
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Abstract
Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem's essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor's web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved.
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