ArticleGigaScience2024
The probability of edge existence due to node degree: a baseline for network-based predictions.
Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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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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.
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Who cites it
11 citing papers in PubMed.
- HetNetEX: Exact Asymptotic Inference in Heterogeneous Biomedical Knowledge Graphs.bioRxiv : the preprint server for biology · 2026Article
- Systematic discovery of disease-modifying targets by prediction from knowledge graph-based AI model and experimental validation: Parkinson's disease case.Computational and structural biotechnology journal · 2026Article
- The role of graph topology in the performance of biomedical Knowledge Graph Completion models.Bioinformatics (Oxford, England) · 2025Article
- 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
- Can AI reveal the next generation of high-impact bone genomics targets?Bone reports · 2025Review
- Network-based anomaly detection algorithm reveals proteins with major roles in human tissues.GigaScience · 2025Article
- Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.Journal of biomedical semantics · 2024Article
- The probability of edge existence due to node degree: a baseline for network-based predictions.GigaScience · 2024Article
- Hetnet connectivity search provides rapid insights into how two biomedical entities are related.bioRxiv : the preprint server for biology · 2023Article
- Hetnet connectivity search provides rapid insights into how biomedical entities are related.GigaScience · 2022Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
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Authors and funding
6 authors.
Funding
Abstract
Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).
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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.