ArticleNature communications2020
A strategy to incorporate prior knowledge into correlation network cutoff selection.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
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- AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction.Communications biology · 2024Article
- A Causal Regulation Modeling Algorithm for Temporal Events with Application toInternational journal of molecular sciences · 2024Article
- Information-incorporated gene network construction with FDR control.Bioinformatics (Oxford, England) · 2024Article
- KNeXT: a NetworkX-based topologically relevant KEGG parser.Frontiers in genetics · 2024Article
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation.Frontiers in molecular biosciences · 2022Review
- Examining the Interaction of the Gut Microbiome with Host Metabolism and Cardiometabolic Health in Metabolic Syndrome.Nutrients · 2021Article
- Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map.Journal of personalized medicine · 2021Article
- Article
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Authors and funding
15 authors at 9 institutions in 7 countries.
Funding
Abstract
Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.
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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.