ArticlePLoS computational biology2016
Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
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
98 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Network and Pathway Analysis of Genes Associated With Atrial Fibrillation.Cardiovascular therapeutics · 2024Pooled it
- A Framework for Benchmarking Pathway Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
- Extracellular matrix-driven patient stratification and network modeling reveal distinct molecular grades with potential clinical implications.NPJ systems biology and applications · 2026Article
- APOE-stratified Proteomic and Metabolomic Analysis Reveals Mitochondrial Dysfunction Inflammation and Lipid Dysregulation in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integration of multiomic and multi-phenotypic data identifies biological pathways associated with physical fitness.Communications biology · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- NOODAI: a webserver for network-oriented multi-omics data analysis and integration pipeline.Bioinformatics (Oxford, England) · 2025Article
- A Holistic Approach to AKI: Integrating Clinical and Molecular Data in the Human Kidney.Seminars in nephrology · 2025Review
- Computational Approaches for Pathway-Centric Analysis of Protein Post-Translational Modifications.Proteomics · 2025Review
- MPAC: a computational framework for inferring pathway activities from multi-omic data.Bioinformatics (Oxford, England) · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Artificial intelligence in the management of metabolic disorders: a comprehensive review.Journal of endocrinological investigation · 2025Review
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons.Neurobiology of disease · 2025Article
- An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration.Nature communications · 2025Article
- CellPHIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology.bioRxiv : the preprint server for biology · 2025Article
- Dysregulation of protein SUMOylation networks in Huntington's disease R6/2 mouse striatum.Brain : a journal of neurology · 2025Article
- Multi-Omic Advances in Olive Tree (Biology · 2025Review
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
High-throughput, 'omic' methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of 'omic' data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.
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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.