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  • An adaptive and computationally efficient machine-learning-based biasing technique for rare-event sampling is introduced, allowing an effective generation of high-dimensional free energy surfaces associated with complex processes, such as protein folding.

    • Mark E. Tuckerman
    News & Views
  • The accurate determination of correlation energy is a challenging task in many-electron quantum chemistry calculations, especially for metals. A recent work proposes an efficient scheme to speed up the calculation of correlation energy, reducing the computational time by up to two orders of magnitude.

    • Jianwei Sun
    News & Views
  • A new study proposes a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network, providing a conceptual overview of the computational properties of this brain network, to show that it is an efficient pattern separator.

    • Ad Aertsen
    News & Views
  • A human mobility model that takes into account social interaction and long-term memory mechanisms is proposed, shedding more light onto the interplay between human movements and urban growth.

    • Pu Wang
    News & Views
  • Compressing scientific data is essential to save on storage space, but doing so effectively while ensuring that the conclusions from the data are not affected remains a challenging task. A recent paper proposes a new method to identify numerical noise from floating-point atmospheric data, which can lead to a more effective compression.

    • Dorit M. Hammerling
    • Allison H. Baker
    News & Views
  • Mobile-phone data reveal a cognitive strategy in human navigation and motivate the development of a new route planning model, with potential implications for traffic forecasting and transportation planning.

    • Laura Alessandretti
    News & Views
  • Finding a parameter that can accurately identify the order–disorder phase transition, especially for complex physical systems with high-dimensional configurational space, is a challenging task. Recent work proposes a machine learning approach to effectively tackle this challenge.

    • Evert van Nieuwenburg
    News & Views
  • An efficient parallelization technique for tensor network contraction, developed by a careful balance between memory requirement and computational time, speeds up classical simulation of quantum computers.

    • Jordi Tura
    News & Views
  • A framework called EPICS predicts microbial community structures by estimating effective pairwise interactions in an efficient and scalable way.

    • Boyang Ji
    • Markus J. Herrgård
    • Jens Nielsen
    News & Views
  • A new study uses longitudinal mobility data to identify how individuals behave at different stages of the COVID pandemic, elucidating benefits and challenges of using this type of data for decision-making by epidemiologists and policy-makers.

    • Nishant Kishore
    News & Views
  • A framework called Detect is proposed to detect subtle effects of brain disorders, making it possible to delineate anomalous brain connections within specific individuals.

    • Ariel Rokem
    News & Views
  • Recent work introduces a powerful new web tool that enables a faster and statistically more reliable data mining of transcriptomics and metatranscriptomics for inflammatory bowel disease (IBD) research.

    • Dezso Modos
    • John P. Thomas
    • Tamas Korcsmaros
    News & Views
  • Modeling of the multiscale dynamics of new bone formation in tissue scaffolds is still challenging due to the computational complexity in solving the mechanics–material–biology interactions. Recent work proposes a machine learning approach to address this challenge.

    • Zhiyong Li
    News & Views
  • A model for Drosophila embryonic development is presented by integrating several types of experimental data spanning over several layers of space and time.

    • Rachel Waymack
    • Zeba Wunderlich
    News & Views
  • Making sense of single-cell data requires various computational efforts such as clustering, visualization and gene regulatory network inference, often addressed by different methods. DeepSEM provides an all-in-one solution.

    • Jun Ding
    News & Views
  • A data-driven approach uses pieces of evidence from existing alloy databases to effectively recommend high-entropy alloys candidates.

    • Houlong Zhuang
    News & Views
  • A graph-neural-network-based framework is proposed for the refinement of protein structure models, substantially improving the efficacy and efficiency of refining protein models when compared with the state-of-the-art approaches.

    • Osama Abdin
    • Philip M. Kim
    News & Views
  • Detection of molecular quantitative trait loci (QTL) facilitates mechanistic insights into disease-associated genetic variants. A new study describes BaseQTL, which exploits allele-specific expression to map molecular QTL from sequencing reads even without paired genotype data.

    • Eric R. Gamazon
    News & Views
  • A study based on effective dimension shows that a quantum neural network can have increased capability and trainability as compared to its classical counterpart.

    • Patrick J. Coles
    News & Views
  • A new format for read depth annotations in genomic data makes access to metadata more scalable and efficient.

    • Mikel Hernaez
    News & Views