ArticleNucleic acids research2014
Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.
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Who cites it
88 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- Design and evaluation of semantically-valid negative samples integration techniques for scalable semi-automated drug repurposing prediction pipelines in rare disease research.BMC bioinformatics · 2026Article
- The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures.Bioinformatics advances · 2026Article
- Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates.Briefings in bioinformatics · 2025Article
- ESM2_AMP: an interpretable framework for protein-protein interactions prediction and biological mechanism discovery.Briefings in bioinformatics · 2025Article
- Article
- Towards a more accurate and reliable evaluation of machine learning protein-protein interaction prediction model performance in the presence of unavoidable dataset biases.Journal of integrative bioinformatics · 2025Article
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Transposable Elements Drive Regulatory and Functional Innovation of F-box Genes.Molecular biology and evolution · 2025Article
- EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals.Nature communications · 2025Article
- Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.Biomolecules · 2025Review
- mimicINT: A workflow for microbe-host protein interaction inference.F1000Research · 2025Article
- An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction.Computational and structural biotechnology journal · 2025Article
- Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions.Computational and structural biotechnology journal · 2025Article
- SpatialPPIv2: Enhancing protein-protein interaction prediction through graph neural networks with protein language models.Computational and structural biotechnology journal · 2025Article
- Proteome-scale recombinant standards and a robust high-speed search engine to advance cross-linking MS-based interactomics.Nature methods · 2024Article
- SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer.Computational and structural biotechnology journal · 2024Article
28 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Knowledge about non-interacting proteins (NIPs) is important for training the algorithms to predict protein-protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at http://mips.helmholtz-muenchen.de/proj/ppi/negatome). Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%.
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