ArticleComputational and structural biotechnology journal2025
gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data.Computational and structural biotechnology journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Count data, such as gene expression and microbiome composition, play a significant role in various diseases, including cancer, obesity, inflammatory bowel disease, and mental health disorders. For instance, understanding the differences in microbial abundance between patients is essential for uncovering the microbiome's impact on these conditions. Differential abundance analysis (DAA) can detect significant changes between groups of patients. However, since individuals have unique microbial fingerprints that could potentially be identifiable, microbiome data must be treated as sensitive patient data, which poses problems for collaborative studies in the medical field. In this work, we introduce gLinDA, a global differential abundance analysis tool that employs a privacy-preserving swarm learning approach for the analysis of distributed datasets. gLinDA maintains predictive performance while safeguarding patient sensitive data.
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.