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Page 2 of 415
    1. Cell Biology
    2. Physics of Living Systems

    KymoButler, a deep learning software for automated kymograph analysis

    Maximilian AH Jakobs et al.
    KymoButler is a machine learning approach towards the reliable, quick, and fully automated analysis of the dynamics of fluorescently labelled particles in living cells.
    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics

    Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning

    Yasuhiro Matsunaga, Yuji Sugita
    A general machine learning scheme for integrating time-series data from single-molecule experiments and molecular dynamics simulations is proposed and successfully demonstrated for the folding dynamics of the WW domain.
    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. Computational and Systems Biology
    2. Physics of Living Systems

    Learning protein constitutive motifs from sequence data

    Jérôme Tubiana et al.
    A new machine-learning toolbox unveils coevolutionary protein motifs related to structure, function, and phylogeny from sequence information only.
    1. Neuroscience

    A machine-vision approach for automated pain measurement at millisecond timescales

    Jessica M Jones et al.
    Development of a fully automated pain scale using machine learning tools in computational neuroethology and creation of new software, reveals a robust circuit-dissection compatible platform for objective pain measurement.
    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.
    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics

    Computational design of thermostabilizing point mutations for G protein-coupled receptors

    Petr Popov et al.
    A comprehensive approach to prediction of stabilizing mutations in G-protein coupled receptors yields high hit rate and crystal structures of 5HT2C in both active and inactive states.
    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. 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. Neuroscience

    Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

    Pengcheng Zhou et al.
    A new open-source computational toolbox for processing in vivo microendoscopic calcium imaging data performs signal demixing and denoising much more accurately than previously available methods, significantly improving the utility of this imaging modality.