ArticleGenomics, proteomics & bioinformatics2020
GTB-PPI: Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting.
Article in Genomics, proteomics & bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Tree-based ensemble learning models for protein-protein interactions detection: a review and experimental evaluation.BioData mining · 2025Review
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- Review
- Carmna: classification and regression models for nitrogenase activity based on a pretrained large protein language model.Briefings in bioinformatics · 2025Article
- Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance.PeerJ. Computer science · 2025Article
- Protein-protein interaction prediction using bidirectional GRUs with explicit ensemble.PloS one · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- Article
- BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.Computational and structural biotechnology journal · 2024Article
- Article
- Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.International journal of molecular sciences · 2024Article
- Analysis and prediction of interactions between transmembrane and non-transmembrane proteins.BMC genomics · 2024Article
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- Computational approaches for the design of modulators targeting protein-protein interactions.Expert opinion on drug discovery · 2023Review
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- A Survey on Deep Networks Approaches in Prediction of Sequence-Based Protein-Protein Interactions.SN computer science · 2022Review
- Research on the Computational Prediction of Essential Genes.Frontiers in cell and developmental biology · 2021Review
- Review
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5 authors.
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Abstract
Protein-protein interactions (PPIs) are of great importance to understand genetic mechanisms, delineate disease pathogenesis, and guide drug design. With the increase of PPI data and development of machine learning technologies, prediction and identification of PPIs have become a research hotspot in proteomics. In this study, we propose a new prediction pipeline for PPIs based on gradient tree boosting (GTB). First, the initial feature vector is extracted by fusing pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), reduced sequence and index-vectors (RSIV), and autocorrelation descriptor (AD). Second, to remove redundancy and noise, we employ L1-regularized logistic regression (L1-RLR) to select an optimal feature subset. Finally, GTB-PPI model is constructed. Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets, respectively. In addition, GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, the one-core PPI network for CD9, and the crossover PPI network for the Wnt-related signaling pathways. The results show that GTB-PPI can significantly improve accuracy of PPI prediction. The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.
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