ArticleBioinformatics (Oxford, England)2026
GAMclust: identification of regulated metabolic modules in bulk, single cell and spatial gene expression data.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
motivationMetabolism operates as a highly interconnected biochemical network, and its regulation emerges from coordinated changes across many reactions and metabolites. The integration of gene expression profiling data with organism-scale metabolic networks has proven to be a valuable tool for understanding cellular metabolic regulation. However, the increasing complexity of profiling technologies and experimental designs requires the development of specialized tools.
resultsHere, we present GAMclust, an R package implementing and extending the previously published GAM-clustering pipeline for identifying transcriptionally regulated metabolic modules in complex gene expression datasets. GAMclust supports bulk, single-cell, and spatial gene expression profiling. It includes built-in KEGG and Rhea metabolic networks for human and mouse, with options to expand these networks for the analysis of other species. The package also offers a suite of post-processing and visualization tools, facilitating the exploration and interpretation of results. AVAILABILITY: GAMclust is freely available at https://github.com/alserglab/GAMclust and https://doi.org/10.5281/zenodo.21432552 under the MIT license. Documentation is available at https://alserglab.github.io/GAMclust. Source code for supplementary materials is available at https://github.com/alserglab/GAMclust-paper.
Indexed as
Identifiers
What OpenQuestion holds
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.