ArticlePLoS computational biology2021
PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 18 citations in OpenAlex.
- Trial
- SpliceImpactR maps alternative RNA processing events driving protein functional diversity.Nucleic acids research · 2026Article
- Mapping of Phenotype Specific Host-Microbiome Protein-Protein Interaction Networks in Colorectal Cancer Using Deep Learning.International journal of molecular sciences · 2026Article
- African Swine Fever Virus R238L and R298L Disrupt Lung Cell Collagen Formation and Cell Adhesion Pathway by Targeting Transcription Factors Containing zf-C2H2 Domain.Veterinary sciences · 2026Article
- ProteoformDB: an integrative database for functional roles of proteoforms.Database : the journal of biological databases and curation · 2026Article
- A guide to building the matrisome interactome: from computational predictions to experimental validation.The FEBS journal · 2026Review
- A machine learning approach to predict strain-specific phage-host interactions.Scientific reports · 2025Article
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
- DIGGER 2.0: digging into the functional impact of differential splicing on human and mouse disorders.Nucleic acids research · 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
- Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.Nature communications · 2024Article
- Virulence network of interacting domains of influenza a and mouse proteins.Frontiers in bioinformatics · 2023Article
- Beneficial bacterial-Frontiers in microbiology · 2022Article
- Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient.Computational and structural biotechnology journal · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called "PPIDM" (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described "CODAC" (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as "Gold-Standard" a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.