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Scallop2 enables a more accurate assembly of transcripts at both single-cell resolution and sample level through a suite of algorithms that leverage the multi-end and paired-end information in Smart-seq3 and Illumina RNA-seq data.
A modeling pipeline for the stochastic binding behavior of antibodies on patterned antigen substrates predicts programmable walking behavior that can be manipulated and directed through pattern geometry.
A Bayesian method is presented for unbiased estimation of timescales from different types of experimental data; the method quantifies the estimation uncertainty and allows for comparing the alternative hypotheses on the underlying dynamics.
The authors propose a two-phase approach to solve the inverse problem of inferring dynamical principles of complex systems from incomplete and noisy data, and apply it to infer the spreading dynamics of H1N1, SARS, and COVID-19.
A fully automated, high-throughput computational framework is presented to predict stable species in liquid solutions. This framework combines density functional theory with classical molecular dynamics simulations to compute the NMR chemical shifts.
The authors demonstrate a robust and rigorous framework that can enumerate up to 100 fluorescent labels in a diffraction limited spot using Bayesian nonparametrics.
mm2-fast is an accelerated version of minimap2, a popular software for long-read data analysis. mm2-fast introduces high-performance parallel computing techniques to reduce the overall runtime of minimap2.
To help determine how life history traits of individuals result in emergent properties of a population, laboratory studies of Caenorhabditis elegans were combined with an individual-based simulation, pointing out to potential factors that influence old age as a cause of death.
A multiscale model is presented to quantitatively predict COVID-19 vaccine efficacies by describing the generation, activity and diversity of neutralizing antibodies.
Networks offer a powerful visual representation of complex systems. This study introduces network visualizations that are easy to interpret and can help explore large datasets, such as the map of all molecular interactions in the cell.
deepManReg uses deep neural networks to map various data types onto a topological space (manifolds) and unfold unseen data connections, thus improving prediction of phenotypes from multi-modal data.
The authors present an open-source framework that enables fast and accurate time–frequency analysis of signals and demonstrate it on real-world applications, such as signals from the brain–computer interface.
Tensor networks exploit the structure of turbulence to offer a compressed description of flows, which leads to efficient fluid simulation algorithms that can be implemented on both classical and quantum computers.
The authors have developed an adaptive reinforced dynamics approach, which improves the efficiency when exploring the configurational space and free energy landscape of large biomolecules, such as proteins.
Defining cell identity is a fundamental task in dissecting the cellular heterogeneity in single-cell data. Here the authors developed Cepo, a method to uncover cell identity genes and enhance the retrieval of cellular identities from scRNA-seq data.
A data-driven solution of partial differential equations is developed with conditional generative adversarial networks, which could be used in both forward and inverse problems.
The authors demonstrate an effective approach to lower the computing time required to accurately reach the thermodynamic limit in quantum many-body calculations. This method can be applied to solve problems in a wide range of material systems, including metals, insulators and semiconductors.
The authors present a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network based on experimental data to show that fast lateral inhibition plays a key role in pattern separation.
The study shows that a memory-aware and socially coupled human movement model can reproduce urban growth patterns at the macro level, providing a bottom-up approach to understand urban growth and to reveal its connection to human mobility behavior.
A machine learning-assisted directed evolution method is developed, combining hierarchical unsupervised clustering and supervised learning, to guide protein engineering by iteratively exploring the large mutational sequence space.