ArticleEvolutionary bioinformatics online2020
Using Random Forest Model Combined With Gabor Feature to Predict Protein-Protein Interaction From Protein Sequence.
Article in Evolutionary bioinformatics online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- VHI-Pred: A Multi-Feature-Based Tool for Predicting Human-Virus Protein-Protein Interactions.Molecular biotechnology · 2026Article
- Protein-protein interaction prediction using bidirectional GRUs with explicit ensemble.PloS one · 2025Article
- An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction.International journal of molecular sciences · 2024Article
- Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning.International journal of molecular sciences · 2023Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
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6 authors.
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Abstract
Protein-protein interactions (PPIs) play a crucial role in the life cycles of living cells. Thus, it is important to understand the underlying mechanisms of PPIs. Although many high-throughput technologies have generated large amounts of PPI data in different organisms, the experiments for detecting PPIs are still costly and time-consuming. Therefore, novel computational methods are urgently needed for predicting PPIs. For this reason, developing a new computational method for predicting PPIs is drawing more and more attention. In this study, we proposed a novel computational method based on texture feature of protein sequence for predicting PPIs. Especially, the Gabor feature is used to extract texture feature and protein evolutionary information from Position-Specific Scoring Matrix, which is generated by Position-Specific Iterated Basic Local Alignment Search Tool. Then, random forest-based classifiers are used to infer the protein interactions. When performed on PPI data sets of
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