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Showing 1–4 of 4 results
Advanced filters: Author: "Aleksej Zelezniak" Clear advanced filters

  • Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.

    • Jan Zrimec
    • Xiaozhi Fu
    • Aleksej Zelezniak
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-17

  • Regulatory and coding regions of genes are shaped by evolution to control expression levels. Here, the authors use deep learning to identify rules controlling gene expression levels and suggest that all parts of the gene regulatory structure interact in this.

    • Jan Zrimec
    • Christoph S. Börlin
    • Aleksej Zelezniak
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-16

  • A protein’s three-dimensional structure and properties are defined by its amino-acid sequence, but mapping protein sequence to protein function is a computationally highly intensive task. A new generative adversarial network approach learns from natural protein sequences and generates new, diverse protein sequence variations, which are experimentally tested.

    • Donatas Repecka
    • Vykintas Jauniskis
    • Aleksej Zelezniak
    Research
    Nature Machine Intelligence
    Volume: 3, P: 324-333