In this paper, we present a federated PCA algorithm for vertically partitioned data which does not exchange the sample eigenvectors and is hence suitable for ...
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Principal component analysis (PCA) is a frequent preprocessing step in GWAS, where the eigenvectors of the sample-by-sample covariance matrix are used as ...
In this paper, we present a federated PCA algorithm for vertically partitioned data which does not exchange the sample eigenvectors and is hence suitable for ...
Federated Principal Component Analysis for Genome-Wide ...
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Although some related research has been proposed to provide secure GWAS, most of them focus on adopting federated methods in χ 2 statistics test [37,39], ...
Dec 2, 2022 · We introduce secure and federated genome-wide association studies (SF-GWAS), a novel combination of secure computation frameworks that empowers efficient and ...
Oct 19, 2023 · This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the ...
We provide implementations of different federated PCA algorithms and evaluate them regarding their accuracy for high-dimensional biological data.
Aug 10, 2024 · SF-GWAS supports the most widely-used GWAS pipelines based on principal component analysis (PCA) or linear mixed models (LMMs). We demonstrate ...
Oct 23, 2023 · In federated learning, standard machine learning (ML) techniques are modified so they can be applied to data held by separate participants ...
Principal component analysis is conceptually the eigendecomposition of the covariance matrix. While there are efficient covariance free algorithms for the ...