ArticleFrontiers in genetics2019
A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis.Bioinformatics (Oxford, England) · 2020Pooled it
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- A review of genetic resources and trends of omics applications in donkey research: focus on China.Frontiers in veterinary science · 2024Review
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- Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function.Journal of translational medicine · 2023Article
- Multi-omics data identified TP53 and LRP1B as key regulatory gene related to immune phenotypes via EPCAM in HCC.Cancer medicine · 2022Article
- Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach.Frontiers in genetics · 2022Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- A Deep-Learning Proteomic-Scale Approach for Drug Design.Pharmaceuticals (Basel, Switzerland) · 2021Article
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- STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline.Frontiers in genetics · 2021Article
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- A Comprehensive Survey of Tools and Software for Active Subnetwork Identification.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.
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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.