ArticleScience advances2024
PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- From molecules to minds: Integrative multi-omics in psychiatry.Journal of mood and anxiety disorders · 2026Review
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- Foundation models in omics research: a comprehensive survey.Briefings in bioinformatics · 2026Review
- scKSFD: federated distillation model with knowledge sharing for cell type classification of clinical transcriptome data.BMC bioinformatics · 2026Article
- Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.Nature communications · 2026Article
- A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers.JMIR AI · 2026Article
- Leveraging Single-Cell Technologies to Advance Understanding of Myocardial Disease.Circulation research · 2026Review
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Rethinking scale in AI-driven genomic medicine - The role of small biobanks.Frontiers in genetics · 2026Article
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- Data visiting governance: a conceptual framework.Human genomics · 2025Review
- Towards an AI biomedical scientist: Accelerating discoveries in neurodegenerative disease.The journal of prevention of Alzheimer's disease · 2025Article
- Research progress of single cell RNA sequencing in nervous system.Molecular biology reports · 2025Review
- Federated Deep Learning Enables Cancer Subtyping by Proteomics.Cancer discovery · 2025Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment.Scientific reports · 2025Article
- An AI Agent for Fully Automated Multi-Omic Analyses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Review
- Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4.Nature communications · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.
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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.