Computational and Systems Biology

Computational and Systems Biology

eLife reviews research involving the use of methods, models and software. Learn more about what we review and sign up for the latest research.
Illustration by Davide Bonazzi

Latest articles

    1. Computational and Systems Biology

    Improved clinical data imputation via classical and quantum determinantal point processes

    Skander Kazdaghli, Iordanis Kerenidis ... Philip Teare
    1. Computational and Systems Biology
    2. Immunology and Inflammation

    Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging

    Alain Pulfer, Diego Ulisse Pizzagalli ... Santiago Fernandez Gonzalez
    ADeS, a novel deep learning approach for apoptosis detection, achieves 98% accuracy in vitro and in vivo while providing unprecedented insights into the spatial–temporal foundation of cell death.
    1. Computational and Systems Biology

    Metabolic model-based ecological modeling for probiotic design

    James D Brunner, Nicholas Chia
    Genome-scale metabolic models can be used to predict the engraftment of probiotics.
    1. Computational and Systems Biology

    Sensitive remote homology search by local alignment of small positional embeddings from protein language models

    Sean R Johnson, Meghana Peshwa, Zhiyi Sun
    Protein language deep learning models can quickly and accurately translate amino acid sequences into profile hidden Markov models or a structure alphabet, dramatically improving remote homology search sensitivity without compromising space or time efficiency.
    1. Cell Biology
    2. Computational and Systems Biology

    Unbiased identification of cell identity in dense mixed neural cultures

    Sarah De Beuckeleer, Tim Van De Looverbosch ... Winnok H. De Vos
    1. Computational and Systems Biology
    2. Immunology and Inflammation

    Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration

    Mathias Fynbo Jensen, Morten Nielsen
    NetTCR 2.2 demonstrates advances in predicting MHC-peptide-TCR interactions through integration of pan- and peptide-specific training strategies, loss-scaling and sequence similarity scoring.

Senior editors

  1. Alan Moses
    University of Toronto, Canada
  2. Aleksandra Walczak
    Ecole Normale Superieure, France
  3. See more editors