ArticleInternational journal of molecular sciences2016
Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 43 citations in OpenAlex.
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- Enhancing cross-domain protein and peptide interaction with retrained deep learning models.Briefings in bioinformatics · 2025Article
- Analysis of PPI networks of transcriptomic expression identifies hub genes associated with Newcastle disease virus persistent infection in bladder cancer.Scientific reports · 2023Article
- ncRPI-LGAT: Prediction of ncRNA-protein interactions with line graph attention network framework.Computational and structural biotechnology journal · 2023Article
- Prediction of protein-protein interaction using graph neural networks.Scientific reports · 2022Article
- Recent advances on the machine learning methods in predicting ncRNA-protein interactions.Molecular genetics and genomics : MGG · 2021Review
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock.BioMed research international · 2020Article
- Using discriminative vector machine model with 2DPCA to predict interactions among proteins.BMC bioinformatics · 2019Article
- A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.PloS one · 2019Article
- An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.Frontiers in genetics · 2019Article
- In silico-prediction of protein-protein interactions network about MAPKs and PP2Cs reveals a novel docking site variants in Brachypodium distachyon.Scientific reports · 2018Article
- PPInS: a repository of protein-protein interaction sitesbase.Scientific reports · 2018Article
- Gene function prediction based on combining gene ontology hierarchy with multi-instance multi-label learning.RSC advances · 2018Article
- Reciprocal Perspective for Improved Protein-Protein Interaction Prediction.Scientific reports · 2018Article
- The PPI network analysis of mRNA expression profile of uterus from primary dysmenorrheal rats.Scientific reports · 2018Article
- IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction.Frontiers in genetics · 2018Article
- Prediction of cassava protein interactome based on interolog method.Scientific reports · 2017Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.
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