ArticleGenome biology2023
COLLAGENE enables privacy-aware federated and collaborative genomic data analysis.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers.JMIR AI · 2026Article
- Metagenomic next-generation sequencing: new horizons in microbiology.Frontiers in cellular and infection microbiology · 2026Review
- NitroGene: privacy-preserving collaborative genomic analysis using AWS nitro enclaves.Bioinformatics advances · 2026Article
- Ethical challenges and governance of artificial intelligence in hepatocellular carcinoma management : Wan DL et al. Ethical considerations in AI for hepatocellular carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Data visiting governance: a conceptual framework.Human genomics · 2025Review
- Proxy panels enable privacy-aware outsourcing of genotype imputation.Genome research · 2025Article
- Review
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- FedGMMAT: Federated generalized linear mixed model association tests.PLoS computational biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .
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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.