March 24 Issue

March issue now live

Bonazzola, R., Ferrante, E., Ravikumar, N. et al. Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology.

Nature Machine Intelligence is a Transformative Journal; authors can publish using the traditional publishing route OR via immediate gold Open Access.

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  • Foundation models have transformed artificial intelligence by training on vast amounts of broad unlabelled data. Pai et al. present a foundation model leading to more accurate, efficient and robust cancer imaging biomarkers, especially in use cases with small training datasets.

    • Suraj Pai
    • Dennis Bontempi
    • Hugo J. W. L. Aerts
    ArticleOpen Access
  • Deep learning generative approaches have been used in recent years to discover new molecules with drug-like properties. To improve the performance of such approaches, Yang et al. add chemical binding knowledge to a deep generative framework and demonstrate, including by wet-lab verification, that the method can find valid molecules that successfully bind to target proteins.

    • Yuanyuan Jiang
    • Guo Zhang
    • Shengyong Yang
    Article
  • This Reusability Report examines a recently published deep learning method PENCIL by Ren et al. for identifying phenotype populations in single-cell data. Cao et al. reproduce here the main results, analyse the sensitivity of the method to model parameters and describe how the method can be used to create a signature for immunotherapy response markers.

    • Yingying Cao
    • Tian-Gen Chang
    • Eytan Ruppin
    Article
  • After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.

    Editorial
  • Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.

    • Marieke Bak
    • Vince I. Madai
    • Stuart McLennan
    Comment
  • Can non-state multinational tech companies counteract the potential democratic deficit in the emerging global governance of AI? We argue that although they may strengthen core values of democracy such as accountability and transparency, they currently lack the right kind of authority to democratize global AI governance.

    • Eva Erman
    • Markus Furendal
    Comment
  • One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.

    Editorial
  • We reflect on five years of Nature Machine Intelligence and on providing a venue for discussions in AI.

    Editorial