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Showing 1–7 of 7 results
Advanced filters: Author: Sarah Kim-Hellmuth Clear advanced filters

  • Swarm Learning is a decentralized machine learning approach that outperforms classifiers developed at individual sites for COVID-19 and other diseases while preserving confidentiality and privacy.

    • Stefanie Warnat-Herresthal
    • Hartmut Schultze
    • Joachim L. Schultze
    ResearchOpen Access
    Nature
    Volume: 594, P: 265-270

  • Using the GTEx data and others, a comprehensive analysis of adenosine-to-inosine RNA editing in mammals is presented; targets of the various ADAR enzymes are identified, as are several potential regulators of editing, such as AIMP2.

    • Meng How Tan
    • Qin Li
    • Jin Billy Li
    Research
    Nature
    Volume: 550, P: 249-254

  • Multiple transcriptome approaches, including single-cell sequencing, demonstrate that escape from X chromosome inactivation is widespread and occasionally variable between cells, chromosomes, and tissues, resulting in sex-biased expression of at least 60 genes and potentially contributing to sex-specific differences in health and disease.

    • Taru Tukiainen
    • Alexandra-Chloé Villani
    • Daniel G. MacArthur
    ResearchOpen Access
    Nature
    Volume: 550, P: 244-248

  • The authors show that rare genetic variants contribute to large gene expression changes across diverse human tissues and provide an integrative method for interpretation of rare variants in individual genomes.

    • Xin Li
    • Yungil Kim
    • Stephen B. Montgomery
    ResearchOpen Access
    Nature
    Volume: 550, P: 239-243

  • Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.

    • Sarah Kim-Hellmuth
    • Matthias Bechheim
    • Veit Hornung
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-10

  • Samples of different body regions from hundreds of human donors are used to study how genetic variation influences gene expression levels in 44 disease-relevant tissues.

    • François Aguet
    • Andrew A. Brown
    • Jingchun Zhu
    ResearchOpen Access
    Nature
    Volume: 550, P: 204-213

  • Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.

    • Alvaro N. Barbeira
    • Scott P. Dickinson
    • Hae Kyung Im
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-20