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Showing 1–8 of 8 results
Advanced filters: Author: Abubakar Abid Clear advanced filters

  • Dimensionality reduction and visualization methods lack a principled way of comparing multiple datasets. Here, Abid et al. introduce contrastive PCA, which identifies low-dimensional structures enriched in one dataset compared to another and enables visualization of dataset-specific patterns.

    • Abubakar Abid
    • Martin J. Zhang
    • James Zou
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-7

  • 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

  • Machine learning models have great potential in biomedical applications. A new platform called GradioHub offers an interactive and intuitive way for clinicians and biomedical researchers to try out models and test their reliability on real-world, out-of-training data.

    • Abubakar Abid
    • Ali Abdalla
    • James Zou
    Comments & Opinion
    Nature Machine Intelligence
    Volume: 2, P: 86-88

  • Large language models, which are increasingly used in AI applications, display undesirable stereotypes such as persistent associations between Muslims and violence. New approaches are needed to systematically reduce the harmful bias of language models in deployment.

    • Abubakar Abid
    • Maheen Farooqi
    • James Zou
    Comments & Opinion
    Nature Machine Intelligence
    Volume: 3, P: 461-463

  • This perspective presents a primer on deep learning applications for the genomics field. It includes a general guide for how to use deep learning and describes the current tools and resources that are available to the community.

    • James Zou
    • Mikael Huss
    • Amalio Telenti
    Reviews
    Nature Genetics
    Volume: 51, P: 12-18