Genomic data holds huge potential for medical progress but requires strict safety measures
due to its sensitive nature to comply with data protection laws. This conflict is especially …

Background Machine learning and artificial intelligence have shown promising results in
many areas and are driven by the increasing amount of available data. However, these data …

Federated machine learning in data-protection-compliant research | Nature Machine Intelligence
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Artificial intelligence (AI) has the potential to transform clinical practice and healthcare.
Following impressive advancements in fields such as computer vision and medical imaging …

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 …

High-throughput sequencing allowed the discovery of many disease variants, but nowadays
it is becoming clear that the abundance of genomics data mostly just moved the bottleneck …

Abstract The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature
unidimensional transformation often used to Gaussianize features in machine learning. In …

Clinical time-to-event studies are dependent on large sample sizes, often not available at a
single institution. However, this is countered by the fact that, particularly in the medical field …

We investigate the concept of utility-preserving federated learning (UPFL) in the context of
deep neural networks. We theoretically prove and experimentally validate that UPFL …

The importance of maintaining data privacy and complying with regulatory requirements is
highlighted especially when sharing omic data between different research centers. This …