ArticlePLoS computational biology2010
Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Discovering the Deregulated Molecular Functions Involved in Malignant Transformation of Endometriosis to Endometriosis-Associated Ovarian Carcinoma Using a Data-Driven, Function-Based Analysis.International journal of molecular sciences · 2017Pooled it
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- Integrative gene ontology-driven analysis of the eutopic endometrium reveals key dysregulated functionomes and pathways in endometriosis.Journal of the Chinese Medical Association : JCMA · 2026Article
- Placental network differences among obstetric syndromes identified with an integrated multiomics approach.Communications biology · 2025Article
- Applications of gene pair methods in clinical research: advancing precision medicine.Molecular biomedicine · 2025Review
- Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers.Communications biology · 2023Article
- The Coaching for Cognition in Alzheimer's (COCOA) trial: Study design.Alzheimer's & dementia (New York, N. Y.) · 2022Article
- Synergistic AHR Binding Pathway with EMT Effects on Serous Ovarian Tumors Recognized by Multidisciplinary Integrated Analysis.Biomedicines · 2021Article
- Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics.International journal of molecular sciences · 2021Article
- Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses.Frontiers in immunology · 2021Article
- The Potential Role of Complement System in the Progression of Ovarian Clear Cell Carcinoma Inferred from the Gene Ontology-Based Immunofunctionome Analysis.International journal of molecular sciences · 2020Article
- The recent progress and therapy in endometriosis-associated ovarian cancer.Journal of the Chinese Medical Association : JCMA · 2020Review
- Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data.Cancer research · 2019Article
- Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning.EBioMedicine · 2019Article
- Key Immunological Functions Involved in the Progression of Epithelial Ovarian Serous Carcinoma Discovered by the Gene Ontology-Based Immunofunctionome Analysis.International journal of molecular sciences · 2018Article
- TLR5 decoy receptor as a novel anti-amyloid therapeutic for Alzheimer's disease.The Journal of experimental medicine · 2018Article
- Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.Bioinformatics (Oxford, England) · 2018Article
- Digitizing omics profiles by divergence from a baseline.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Integrating the dysregulated inflammasome-based molecular functionome in the malignant transformation of endometriosis-associated ovarian carcinoma.Oncotarget · 2018Article
- Comparative transcriptomic analysis of human placentae at term and preterm delivery.Biology of reproduction · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A powerful way to separate signal from noise in biology is to convert the molecular data from individual genes or proteins into an analysis of comparative biological network behaviors. One of the limitations of previous network analyses is that they do not take into account the combinatorial nature of gene interactions within the network. We report here a new technique, Differential Rank Conservation (DIRAC), which permits one to assess these combinatorial interactions to quantify various biological pathways or networks in a comparative sense, and to determine how they change in different individuals experiencing the same disease process. This approach is based on the relative expression values of participating genes-i.e., the ordering of expression within network profiles. DIRAC provides quantitative measures of how network rankings differ either among networks for a selected phenotype or among phenotypes for a selected network. We examined disease phenotypes including cancer subtypes and neurological disorders and identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. At a sample level, DIRAC can detect a change in ranking between phenotypes for any selected network. Variably expressed networks represent statistically robust differences between disease states and serve as signatures for accurate molecular classification, validating the information about expression patterns captured by DIRAC. Importantly, DIRAC can be applied not only to transcriptomic data, but to any ordinal data type.
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