ArticleBMC bioinformatics2007
A domain-based approach to predict protein-protein interactions.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Mapping of Phenotype Specific Host-Microbiome Protein-Protein Interaction Networks in Colorectal Cancer Using Deep Learning.International journal of molecular sciences · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Relational subgraphs fused with complete subgraphs based on the knowledge graph for mining protein complexes.Scientific reports · 2025Article
- Structure-Based Modeling of the Gut Bacteria-Host Interactome Through Statistical Analysis of Domain-Domain Associations Using Machine Learning.Biotech (Basel (Switzerland)) · 2025Article
- Structure-Based Deep Learning Framework for Modeling Human-Gut Bacterial Protein Interactions.Proteomes · 2025Article
- Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.Biomolecules · 2025Review
- Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions.Computational and structural biotechnology journal · 2024Article
- Reconstruction ofCurrent issues in molecular biology · 2024Article
- Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions.bioRxiv : the preprint server for biology · 2024Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.PloS one · 2023Article
- LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor.PloS one · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions.PLoS computational biology · 2021Article
- Article
- An Efficient Computational Model for Large-Scale Prediction of Protein-Protein Interactions Based on Accurate and Scalable Graph Embedding.Frontiers in genetics · 2021Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.Computational and structural biotechnology journal · 2020Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway.Scientific reports · 2017Article
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2 authors.
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Abstract
backgroundKnowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.
resultsDomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.
conclusionWe envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.
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