Motivation Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by spe...
Valentin Schneider-Lunitz, Landfried Kraatz, Patrick Magee et al.· bioRxiv· 0 citations
A large-scale international programme—the Human Cancer Models Initiative—is presented, which involved the generation of a resource of 665 next-generation models from 2,780 donors with 25 cancer types and integrated tumour–model whole genome, exome, methylome and transcriptome analyses.
Dina Elharouni, Mushriq Al-Jazrawe, Seongmin Choi et al.· Nature· 7 citations
Ten tips for successfully and sustainably implementing Federated learning for Biomedical applications, ensuring both ethical data governance and improved model performance in sensitive domains are outlined.
Kyle Ellrott, V. Malladi, J. Bélisle-Pipon et al.· PLoS Computational Biology· 0 citations
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