ArticlePatterns (New York, N.Y.)2024
Federated learning for multi-omics: A performance evaluation in Parkinson's disease.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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Who cites it
16 citing papers in PubMed, 22 citations in OpenAlex.
- Article
- From molecules to minds: Integrative multi-omics in psychiatry.Journal of mood and anxiety disorders · 2026Review
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Privacy-preserving federated prediction of health outcomes using multi-center survey data.BMC medical research methodology · 2026Article
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- Privacy and personalisation: predicting Parkinson's disease severity from real-world gait with federated learning.Frontiers in aging neuroscience · 2026Article
- Data visiting governance: a conceptual framework.Human genomics · 2025Review
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Integrative Omics and AI-Driven Systems Biology: Multilayer Networks DecodingJournal of proteome research · 2025Review
- Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.Clinical and experimental medicine · 2025Review
- Dualistic Dynamics in Neuropsychiatry: From Monoaminergic Modulators to Multiscale Biomarker Maps.Biomedicines · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.Frontiers in digital health · 2025Review
- Accelerating Parkinson's Disease drug development with federated learning approaches.NPJ Parkinson's disease · 2024Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open-source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.
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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 OpenQuestion graph.