ArticleBioData mining2018
PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia.
Article in BioData mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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Who cites it
9 citing papers in PubMed.
- Article
- Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia.mSystems · 2023Article
- SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.Genomics, proteomics & bioinformatics · 2022Article
- An automatic integrative method for learning interpretable communities of biological pathways.NAR genomics and bioinformatics · 2022Article
- Cleaning Quality Control Management of Medical Equipment in Hospital Disinfection Supply Room Based on Smart Medicine.Computational intelligence and neuroscience · 2022Article
- Using genome-wide expression compendia to study microorganisms.Computational and structural biotechnology journal · 2022Review
- Microbial Assemblage Dynamics Within the American Alligator Nesting Ecosystem: a Comparative Approach Across Ecological Scales.Microbial ecology · 2020Article
- Conditional antagonism in co-cultures of Pseudomonas aeruginosa and Candida albicans: An intersection of ethanol and phosphate signaling distilled from dual-seq transcriptomics.PLoS genetics · 2020Article
- Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone.BMC systems biology · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundInvestigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.
resultsWe developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as
conclusionsThe PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.
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Registered trials
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