ArticleBMC bioinformatics2016
Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Remaining useful life prediction across different operational conditions based on domain adaptation feature transfer.Scientific reports · 2026Article
- Kernel mean matching enhances risk estimation under spatial distribution shifts.Scientific reports · 2026Article
- Conditional generative modeling for de novo protein design with hierarchical functions.Bioinformatics (Oxford, England) · 2022Article
- Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy.BMC medical genomics · 2019Article
- Phenotype-driven identification of epithelial signalling clusters.Scientific reports · 2018Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two samples are from the same distribution, its implementation being greatly simplified using the kernel method.
resultsAn MMD-based test successfully detected the differential expression between two conditions, specifically the expression of a set of genes involved in certain fatty acid metabolic pathways. Furthermore, we exploited the ability of the kernel method to integrate data and successfully added hepatic fatty acid levels to the test procedure.
conclusionMMD is a non-parametric test that acquires several advantages when combined with the kernelization of data: 1) the number of variables can be greater than the sample size; 2) omics data can be integrated; 3) it can be applied not only to vectors, but to strings, sequences and other common structured data types arising in molecular biology.
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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.