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  • A limitation of data obtained from RNA-seq experiments is the presence of different types of cell expression, making it difficult to identify the contribution of cell-type composition or cell-type-specific expression. A new study addresses this problem by proposing a method for cell-type-aware analysis of RNA-seq data.

    • Dvir Aran
    News & Views
  • The computational complexity of deep neural networks is a major obstacle of many application scenarios driven by low-power devices, including federated learning. A recent finding shows that random sketches can substantially reduce the model complexity without affecting prediction accuracy.

    • Shiqiang Wang
    News & Views
  • Sharing of genomic data poses a problem due to privacy concerns and lack of individual’s choice in the matter. In this issue, researchers propose a framework for sharing data where the control lies with the individual.

    • Fida K. Dankar
    News & Views
  • A model for the electrical double layer at solid-state electrochemical interfaces is reported, shedding some light on the design and optimization of future all-solid-state Li-ion batteries.

    • Sokseiha Muy
    • Nicola Marzari
    News & Views
  • The advent of STED microscopy, which allows observation at a sub-diffraction resolution, raises a challenge in studying spatial proximities of biomolecules’ distributions. In this issue, researchers have attempted to study colocalization of molecules by employing optimal transport.

    • Shulei Wang
    • Ming Yuan
    News & Views
  • The identification of genes that are associated with developmental trajectories of cells is an important focus in single-cell transcriptomics. This issue presents Scellnetor, a resource that facilitates this task.

    • Florian Klimm
    News & Views
  • Understanding and quantifying the uncertainty of predictions from COVID-19 pandemic models is essential to inform public health decision making. This issue presents one such examination using the influential CovidSim model.

    • Kathy Leung
    • Joseph T. Wu
    News & Views
  • Obtaining a consistent taxonomy of neuron types is challenging mainly because of the high dimensionality of the datasets. Coupled autoencoders are a step forward in achieving this goal.

    • Stephane Bugeon
    News & Views
  • The mechanisms facilitating evolutionary adaptation to future challenges are difficult to establish experimentally. Recent computational simulations of 200 cell populations indicate how evolution can hide useless genetic switches with capacity for later use.

    • Gábor Balázsi
    News & Views
  • The nature of biological networks still brings challenges related to computational complexity, interpretable results and statistical significance. Recent work proposes a new method that paves the way for addressing these issues when analyzing cancer genomic data.

    • Hanna Najgebauer
    • Umberto Perron
    • Francesco Iorio
    News & Views
  • Characterizing the aggregation of the peptide amyloid β is essential to better understand Alzheimer’s disease and to find potential targets for drug development. Deep neural networks make it possible to describe the kinetics of this peptide, opening the way for achieving this goal.

    • Fanjie Meng
    • Hoi Sung Chung
    News & Views