ArticleBioinformatics (Oxford, England)2016
HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.Bioinformatics (Oxford, England) · 2026Article
- Shaping the Plant Specialized Metabolites Through Modern Breeding Technique.Molecular biotechnology · 2026Review
- PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.Bioinformatics (Oxford, England) · 2025Article
- Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS.iScience · 2025Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- Genomic privacy and security in the era of artificial intelligence and quantum computing.Discover computing · 2025Review
- SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine.Genome biology · 2024Article
- Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS.bioRxiv : the preprint server for biology · 2024Article
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- Ultra-secure storage and analysis of genetic data for the advancement of precision medicine.bioRxiv : the preprint server for biology · 2024Article
- PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem.BMC bioinformatics · 2023Article
- Sociotechnical safeguards for genomic data privacy.Nature reviews. Genetics · 2022Review
- Efficient verification for outsourced genome-wide association studies.Journal of biomedical informatics · 2021Article
- Strategies to Modulate Specialized Metabolism in Mediterranean Crops: From Molecular Aspects to Field.International journal of molecular sciences · 2021Review
- A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data.Computational and structural biotechnology journal · 2021Article
- A secure system for genomics clinical decision support.Journal of biomedical informatics · 2020Article
- Privacy-preserving approximate GWAS computation based on homomorphic encryption.BMC medical genomics · 2020Article
- Privacy-preserving construction of generalized linear mixed model for biomedical computation.Bioinformatics (Oxford, England) · 2020Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
motivationGenome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size.
resultsWe target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. AVAILABILITY AND IMPLEMENTATION: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.