ArticleBMC genomics2009
Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.
Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Age-related risk factors and severity of SARS-CoV-2 infection: a systematic review and meta-analysis.Journal of preventive medicine and hygiene · 2021Pooled it
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- Multiomics of Aging and Aging-Related Diseases.International journal of molecular sciences · 2024Review
- MoNETA: MultiOmics Network Embedding for SubType Analysis.NAR genomics and bioinformatics · 2024Article
- An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors.Molecular systems biology · 2024Article
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
- Building composite indices in the age of big data - Application to honey bee exposure to infectious and parasitic agents.Heliyon · 2023Article
- Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators.Frontiers in psychiatry · 2023Article
- Was China's zero-COVID policy the right choice? The multiple factor analysis of variables that affected the course of COVID-19 pandemic in China.Frontiers in public health · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.Cell reports. Medicine · 2022Review
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Individualized multi-omic pathway deviation scores using multiple factor analysis.Biostatistics (Oxford, England) · 2022Article
- Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model.Frontiers in molecular biosciences · 2022Article
- Functional impact of multi-omic interactions in breast cancer subtypes.Frontiers in genetics · 2022Article
- Omics Biomarkers for Monitoring Tuberculosis Treatment: A Mini-Review of Recent Insights and Future Approaches.Infection and drug resistance · 2022Review
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.
resultsHere, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.
conclusionWhen applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.
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