ArticleInterface focus2013
hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests.
Article in Interface focus, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Identification of Common Genes Regulated by ER Stress During the Development of Diabetic Nephropathy Based on Human Transcriptome Datasets and an In Vivo Mouse Model.International journal of molecular sciences · 2026Article
- Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.Cell genomics · 2023Article
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- Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges.Cancers · 2021Review
- Information Theory in Computational Biology: Where We Stand Today.Entropy (Basel, Switzerland) · 2020Article
- Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer (Invited Paper).IEEE transactions on molecular, biological, and multi-scale communications · 2018Article
- Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm.Genes · 2018Article
- Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.Proteomics · 2017Review
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.PLoS computational biology · 2017Article
- The pan-cancer pathological regulatory landscape.Scientific reports · 2016Article
- Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists.Bioinformatics and biology insights · 2015Review
- Systems biology. Conditional density-based analysis of T cell signaling in single-cell data.Science (New York, N.Y.) · 2014Article
- Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis.BMC systems biology · 2014Article
- Large differences in global transcriptional regulatory programs of normal and tumor colon cells.BMC cancer · 2014Article
- Estimating the activity of transcription factors by the effect on their target genes.Bioinformatics (Oxford, England) · 2014Article
- Article
- Inferring protein modulation from gene expression data using conditional mutual information.PloS one · 2014Article
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3 authors.
Funding
Abstract
A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
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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.