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    1. Plant Biology

    Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth

    Martial Sankar et al.
    Combining high-resolution imaging with automated image segmentation and supervised machine learning achieves accurate cellular feature extraction and automated cell type recognition in a large-scale developmental process.
    1. Human Biology and Medicine
    2. Neuroscience

    Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

    Denis A Engemann et al.
    Predicting age jointly from multimodal brain images and electrophysiology with machine learning enhances detecting health issues and facets of cognitive decline.
    1. Structural Biology and Molecular Biophysics

    Top-down machine learning approach for high-throughput single-molecule analysis

    David S White et al.
    A new analysis algorithm (DISC) enables accurate analysis of data from high-throughput single-molecule paradigms and reveals a non-cooperative binding mechanism of cyclic nucleotide-binding domains from HCN ion channels.
    1. Cell Biology
    2. Developmental Biology

    CytoCensus, mapping cell identity and division in tissues and organs using machine learning

    Martin Hailstone et al.
    Easy-to-use image analysis software enables single cell quantitation of cell types and division rates in complex 3D tissues including living Drosophila brains, mouse embryos and Zebrafish organoids.
  1. Open-Source Community Call: AI and machine-learning technologies for open research

    We share a recording of our discussion on the recent efforts and opportunities in applying cutting-edge technologies to research communication.
    1. Computational and Systems Biology

    A computational interactome and functional annotation for the human proteome

    José Ignacio Garzón et al.
    A machine-learning approach is used to predict 1.35 million interactions for 85% of the human proteome.
    1. Computational and Systems Biology
    2. Ecology

    Data-driven identification of potential Zika virus vectors

    Michelle V Evans et al.
    Data-driven methods predict over 35 mosquitoes are potential vectors of Zika virus, suggesting a larger geographic area and a greater human population is at risk of infection.
    1. Computational and Systems Biology
    2. Neuroscience

    Discovering and deciphering relationships across disparate data modalities

    Joshua T Vogelstein et al.
    Multiscale Graph Correlation, an interpretable hypothesis test with strong theoretical guarantees for discerning relationships in complex data, requires about half the sample size as other methods, whilst maintaining computational tractability.
    1. Computational and Systems Biology
    2. Evolutionary Biology

    Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases

    Rahil Taujale et al.
    Deep mining of GT-A fold sequences provides an evolutionary framework for investigating complex relationships connecting GT-A fold sequence, structure, function and regulation.