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Showing 1–19 of 19 results
Advanced filters: Author: Caroline Uhler Clear advanced filters

  • Hodgkin Reed Sternberg (HRS) cells and their surrounding microenvironment in Hodgkin lymphoma remain poorly characterized. Here, the authors perform genome-wide transcriptional profiling with spatial and single-cell resolution to explore the cellular and molecular composition of the Hodgkin lymphoma microenvironment and used machine learning to identify IL13 as a potential HRS cell survival factor.

    • Vignesh Shanmugam
    • Neriman Tokcan
    • Todd R. Golub
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-17

  • Prediction of Unseen Proteins’ Subcellular localization (PUPS) combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images for predicting protein localization on unseen proteins in a way that captures single-cell variability and cell-type specificity.

    • Xinyi Zhang
    • Yitong Tseo
    • Caroline Uhler
    Research
    Nature Methods
    Volume: 22, P: 1265-1275

  • Monitoring the hallmarks and progression of ductal carcinoma in situ (DCIS) remains challenging. Here, the authors use an unsupervised representation learning approach on chromatin images to discern multiple morphological cell states and tissue features in DCIS.

    • Xinyi Zhang
    • Saradha Venkatachalapathy
    • G. V. Shivashankar
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-16

  • 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

  • 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

  • Identifying interventions that can induce a desired effect is challenging owing to the combinatorial number of possible choices in design space. Zhang and colleagues propose an active learning approach with theoretical guarantees to discover optimal interventions in causal models, and demonstrate the framework in the context of genetic perturbation design using single-cell transcriptomic data.

    • Jiaqi Zhang
    • Louis Cammarata
    • Caroline Uhler
    Research
    Nature Machine Intelligence
    Volume: 5, P: 1066-1075

  • 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

  • Methods for jointly analysing the different spatial data modalities in 3D are lacking. Here the authors report the computational framework STACI (Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data) which they apply to an Alzheimer’s disease mouse model.

    • Xinyi Zhang
    • Xiao Wang
    • Caroline Uhler
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-17

  • Accurate prediction of complex systems such as protein folding, weather forecasting and social dynamics is a core challenge in various disciplines. By fusing machine learning algorithms and classic equation-free methodologies, it is possible to reduce the computational effort of large-scale simulations by up to two orders of magnitude while maintaining the prediction accuracy of the full system dynamics.

    • Pantelis R. Vlachas
    • Georgios Arampatzis
    • Petros Koumoutsakos
    Research
    Nature Machine Intelligence
    Volume: 4, P: 359-366

  • 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

  • Mechanical cues from the microenvironment can be efficiently transmitted to the nucleus to engage in the regulation of genome organization and gene expression. Recent technological and theoretical progress sheds new light on the relationships between cell mechanics, nuclear and chromosomal architecture and gene transcription.

    • Caroline Uhler
    • G. V. Shivashankar
    Reviews
    Nature Reviews Molecular Cell Biology
    Volume: 18, P: 717-727

  • With biomedical sciences quickly outgrowing many other application areas in terms of data generation, there is a unique opportunity for life sciences to become one of the greatest beneficiaries of research in machine learning and AI, and also inspire foundational developments in it.

    • Caroline Uhler
    Comments & Opinion
    Nature Cell Biology
    Volume: 26, P: 13-14