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Showing 1–23 of 23 results
Advanced filters: Author: Jure Leskovec Clear advanced filters

  • A huge smartphone dataset of physical activity yields global insights, revealing that activity inequality predicts obesity better than does volume of activity and that much of the inequality is a result of reduced activity in females.

    • Tim Althoff
    • Rok Sosič
    • Jure Leskovec
    Research
    Nature
    Volume: 547, P: 336-339

  • STELLAR (spatial cell learning) is a geometric deep learning model that works with spatially resolved single-cell datasets to both assign cell types in unannotated datasets based on a reference dataset and discover new cell types.

    • Maria Brbić
    • Kaidi Cao
    • Jure Leskovec
    Research
    Nature Methods
    Volume: 19, P: 1411-1418

  • Trained on a medical knowledge graph, a foundation model is used to rank drugs as potential indications and contraindications across 17,080 diseases, identifying therapeutic candidates in a zero-shot framework even for diseases with limited treatment options or no existing drugs and outperforming existing models by a large margin.

    • Kexin Huang
    • Payal Chandak
    • Marinka Zitnik
    ResearchOpen Access
    Nature Medicine
    Volume: 30, P: 3601-3613

  • There is extreme socioeconomic segregation in large US cities, arising from a greater choice of differentiated spaces targeted to specific socioeconomic groups, which can be countered by positioning city hubs (such as shopping centres) to bridge diverse neighbourhoods.

    • Hamed Nilforoshan
    • Wenli Looi
    • Jure Leskovec
    ResearchOpen Access
    Nature
    Volume: 624, P: 586-592

  • Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins; how drugs restore these functions, however, is often unknown. Here, the authors develop the multiscale interactome, a powerful approach to explain disease treatment.

    • Camilo Ruiz
    • Marinka Zitnik
    • Jure Leskovec
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-15

  • An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.

    • Serina Chang
    • Emma Pierson
    • Jure Leskovec
    Research
    Nature
    Volume: 589, P: 82-87

  • Human behaviour and physiology show cycles of variation. Here, using 241 million observations from 3.3 million women across 109 countries, the authors show that, out of the daily, weekly, seasonal and menstrual cycles, the menstrual cycle had the greatest magnitude for most dimensions of mood, behaviour and vital signs.

    • Emma Pierson
    • Tim Althoff
    • Jure Leskovec
    Research
    Nature Human Behaviour
    Volume: 5, P: 716-725

  • Technical noise in experiments is unavoidable, but it introduces inaccuracies into the biological networks we infer from the data. Here, the authors introduce a diffusion-based method for denoising undirected, weighted networks, and show that it improves the performances of downstream analyses.

    • Bo Wang
    • Armin Pourshafeie
    • Jure Leskovec
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-8

  • Studying diets is challenging, typically restricted to small sample sizes, single locations, and non-uniform design across studies. Here, the authors leverage food entry data of a popular diet tracking app to observe diet health and weight status, studying the associations of fast food and grocery access, income and education with diet health outcomes.

    • Tim Althoff
    • Hamed Nilforoshan
    • Jure Leskovec
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12

  • Intestinal cell types are organized into distinct neighbourhoods and communities within the healthy human intestine, with distinct immunological niches.

    • John W. Hickey
    • Winston R. Becker
    • Michael Snyder
    ResearchOpen Access
    Nature
    Volume: 619, P: 572-584

  • Classifying cells into unseen cell types remains challenging in scRNA-seq analysis. Here we show that Cell Ontology enables an accurate classification of unseen cell types through considering the cell type relationships in the Cell Ontology graph.

    • Sheng Wang
    • Angela Oliveira Pisco
    • Russ B. Altman
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-11

  • Community detection allows one to decompose a network into its building blocks. While communities can be identified with a variety of methods, their relative importance can’t be easily derived. Here the authors introduce an algorithm to identify modules which are most promising for further analysis.

    • Marinka Zitnik
    • Rok Sosič
    • Jure Leskovec
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-9

  • An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity.

    • Emma Pierson
    • David M. Cutler
    • Ziad Obermeyer
    Research
    Nature Medicine
    Volume: 27, P: 136-140

  • Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

    • Kexin Huang
    • Tianfan Fu
    • Marinka Zitnik
    Comments & Opinion
    Nature Chemical Biology
    Volume: 18, P: 1033-1036

  • This review discusses generalist medical artificial intelligence, identifying potential applications and setting out specific technical capabilities and training datasets necessary to enable them, as well as highlighting challenges to its implementation.

    • Michael Moor
    • Oishi Banerjee
    • Pranav Rajpurkar
    Reviews
    Nature
    Volume: 616, P: 259-265

  • The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.

    • Hanchen Wang
    • Tianfan Fu
    • Marinka Zitnik
    Reviews
    Nature
    Volume: 620, P: 47-60