Browse our latest Computational and Systems Biology articles

Page 6 of 125
    1. Epidemiology and Global Health
    2. Computational and Systems Biology

    Refining uncertainty about the TAK-003 dengue vaccine with a multi-level model of clinical efficacy trial data

    Manar Alkuzweny, Guido España, T Alex Perkins
    Not revised
    Reviewed Preprint v1
    • Important
    • Convincing
    1. Neuroscience
    2. Computational and Systems Biology

    Comparing the outputs of intramural and extramural grants funded by National Institutes of Health

    Xiang Zheng, Qiyao Yang ... B Ian Hutchins
    Not revised
    Reviewed Preprint v1
    • Important
    • Solid
    1. Cell Biology
    2. Computational and Systems Biology

    Lipid packing contributes to the confinement of caveolae to the plasma membrane

    Elin Larsson, Aleksei Kabedev ... Richard Lundmark
    Not revised
    Reviewed Preprint v1
    • Important
    • Solid
    • Incomplete
    1. Computational and Systems Biology
    2. Evolutionary Biology

    Importance of higher-order epistasis in protein sequence-function relationships

    Palash Sethi, Juannan Zhou
    Not revised
    Reviewed Preprint v1
    • Important
    • Solid
    1. Computational and Systems Biology

    Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development

    César Nieto, Zahra Vahdat ... Abhyudai Singh
    Not revised
    Reviewed Preprint v1
    • Valuable
    • Solid
    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology

    Macroscopic Analyses of RNA-Seq Data to Reveal Chromatin Modifications in Aging and Disease

    Achal Mahajan, Francesca Ratti ... Vishrawas Gopalakrishnan
    Not revised
    Reviewed Preprint v1
    • Valuable
    • Incomplete
    1. Computational and Systems Biology
    2. Neuroscience

    Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

    Christopher M Kim, Carson C Chow, Bruno B Averbeck
    Neural activity during reversal learning encodes decision-related evidence integrated across trials and shows substantial dynamics during each trial, suggesting an extension of the line attractor model that incorporates non-stationary dynamics.