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Showing 1–6 of 6 results
Advanced filters: Author: Adityanarayanan Radhakrishnan Clear advanced filters

  • A challenge in diagnostics is integrating different data modalities to characterize physiological state. Here, the authors show, using the heart as a model system, that cross-modal autoencoders can integrate and translate modalities to improve diagnostics and identify associated genetic variants.

    • Adityanarayanan Radhakrishnan
    • Sam F. Friedman
    • Caroline Uhler
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-12

  • Transfer learning can be applied in computer vision and natural language processing to utilize knowledge from a source task to improve performance on a target task. The authors propose a framework for transfer learning with kernel methods for improved image classification and virtual drug screening.

    • Adityanarayanan Radhakrishnan
    • Max Ruiz Luyten
    • Caroline Uhler
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-12

  • Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. Here, the authors identify robust druggable protein targets within a principled causal framework that makes use of multiple data modalities and integrates aging signatures.

    • Anastasiya Belyaeva
    • Louis Cammarata
    • Caroline Uhler
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-13

  • Integration of single cell data modalities increases the richness of information about the heterogeneity of cell states, but integration of imaging and transcriptomics is an open challenge. Here the authors use autoencoders to learn a probabilistic coupling and map these modalities to a shared latent space.

    • Karren Dai Yang
    • Anastasiya Belyaeva
    • Caroline Uhler
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-10