ArticleGenome biology2012
A computational framework for boosting confidence in high-throughput protein-protein interaction datasets.
Article in Genome biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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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.
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Who cites it
28 citing papers in PubMed.
- A conserved regulatory architecture stabilizes cellular senescence across distinct triggers in human fibroblasts.GeroScience · 2026Article
- A graph neural network-based approach for predicting SARS-CoV-2-human protein interactions from multiview data.PloS one · 2025Article
- Deciphering the the molecular mechanism of aloe-emodin in managing type II diabetes mellitus using network pharmacology, molecular docking, and molecular dynamics simulation approaches.In silico pharmacology · 2025Article
- AKT Phosphorylates FAM13A and Promotes Its Degradation via CUL4A/DDB1/DCAF1 E3 Complex.American journal of respiratory cell and molecular biology · 2023Article
- ADH-PPI: An attention-based deep hybrid model for protein-protein interaction prediction.iScience · 2022Article
- PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.Journal of molecular biology · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions.Cell systems · 2021Article
- Deep learning integration of molecular and interactome data for protein-compound interaction prediction.Journal of cheminformatics · 2021Article
- Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers.International journal of molecular sciences · 2021Review
- Methods for Molecular Modelling of Protein Complexes.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Amalgamation of 3D structure and sequence information for protein-protein interaction prediction.Scientific reports · 2020Article
- Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks.International journal of molecular sciences · 2019Article
- Computational identification of protein-protein interactions in model plant proteomes.Scientific reports · 2019Article
- An integration of deep learning with feature embedding for protein-protein interaction prediction.PeerJ · 2019Article
- Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources.BMC bioinformatics · 2017Article
- Network-based machine learning and graph theory algorithms for precision oncology.NPJ precision oncology · 2017Review
- Conservation of coevolving protein interfaces bridges prokaryote-eukaryote homologies in the twilight zone.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Prediction of Protein-Protein Interactions by Evidence Combining Methods.International journal of molecular sciences · 2016Review
- HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1.Molecular & cellular proteomics : MCP · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer -related or damaging SNPs. Coev2Net can be downloaded at http://struct2net.csail.mit.edu.
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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.