SynthesisGenome biology2021
Flimma: a federated and privacy-aware tool for differential gene expression analysis.
Synthesis in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.BioData mining · 2026Article
- Next-generation sequencing: Driving a paradigm shift from pathogen detection to systems-level analysis.Infectious medicine · 2026Article
- Privacy-preserving multicenter differential protein abundance analysis with FedProt.Nature computational science · 2025Article
- FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data.Genome biology · 2025Article
- Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.Frontiers in digital health · 2025Review
- Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.PLoS computational biology · 2024Article
- AI in microbiome-related healthcare.Microbial biotechnology · 2024Review
- Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility.Genome biology · 2024Review
- Article
- PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.Science advances · 2024Article
- Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.Scientific reports · 2023Article
- Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.Heliyon · 2023Article
- The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.Journal of medical Internet research · 2023Article
- dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.Bioinformatics (Oxford, England) · 2022Article
- Privacy-aware multi-institutional time-to-event studies.PLOS digital health · 2022Article
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
Indexed as
Identifiers
What Socratic 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 Socratic graph.