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Showing 1–6 of 6 results
Advanced filters: Author: Maria Xenochristou Clear advanced filters

  • Culos, Manas et al. construct machine learning surrogate frailty metrics from activity data collected in a nominally intrusive manner. They show that a limited amount of activity data is necessary to model frailty metrics allowing for an increased proliferation of their application along with a robust source of data for other age-related outcomes.

    • Anthony Culos
    • Asier Manas
    • Nima Aghaeepour
    ResearchOpen Access
    Communications Medicine
    Volume: 6, P: 1-7

  • Single-cell technologies are increasingly prominent in clinical applications, but predictive modelling with such data in large cohorts has remained computationally challenging. We developed a new algorithm, ‘VoPo’, for predictive modelling and visualization of single cell data for translational applications.

    • Natalie Stanley
    • Ina A. Stelzer
    • Nima Aghaeepour
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-9

  • Recent advances have increased the dimensionality and complexity of immunological data. The authors developed a machine learning approach to incorporate prior immunological knowledge and applied it on clinical examples and a simulation study. The approach may be useful for high-dimensional datasets in clinical settings where the cohort size is limited.

    • Anthony Culos
    • Amy S. Tsai
    • Nima Aghaeepour
    Research
    Nature Machine Intelligence
    Volume: 2, P: 619-628

  • Federated learning can be used to train medical AI models on sensitive personal data while preserving important privacy properties; however, the sensitive nature of the data makes it difficult to evaluate approaches reproducibly on real data. The MedPerf project presented by Karargyris et al. provides the tools and infrastructure to distribute models to healthcare facilities, such that they can be trained and evaluated in realistic settings.

    • Alexandros Karargyris
    • Renato Umeton
    • Peter Mattson
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 5, P: 799-810