ArticleeLife2024
Emergence of power law distributions in protein-protein interaction networks through study bias.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Inferring and evaluating network medicine-based disease modules with nextflow.Bioinformatics (Oxford, England) · 2026Article
- Beyond gene length: Exon-intron architecture and isoform potential in the evolution of eukaryotic complexity.bioRxiv : the preprint server for biology · 2026Article
- SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.Briefings in bioinformatics · 2026Article
- Towards a phylogenetically informed approach to solving protein-protein interactions.Biochemical Society transactions · 2025Review
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Emergence of power law distributions in protein-protein interaction networks through study bias.eLife · 2024Article
- Target repositioning using multi-layer networks and machine learning: The case of prostate cancer.Computational and structural biotechnology journal · 2024Article
- Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.
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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.