ArticleGigaScience2022
Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 8 citations in OpenAlex.
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- Article
- Patient-specific analysis of co-expression to measure biological network rewiring in individuals.Life science alliance · 2024Article
- Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data.GigaScience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques.
findingsWe show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein-protein interaction networks assayed in different cell lines.
conclusionsThese examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques.
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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.