Research articles

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  • An end-to-end machine learning approach that can learn which mechanisms determine cell fate and competition from a large time-lapse microscopy dataset is developed. The approach makes use of a probabilistic autoencoder to learn an interpretable representation of the organization of cells, and provides cell fate predictions that can be tested in drug screening experiments.

    • Christopher J. Soelistyo
    • Giulia Vallardi
    • Alan R. Lowe
  • Deep learning methods can provide useful predictions for drug design, but their hyperparameters need to be carefully tweaked to give good performance on a specific problem or dataset. Li et al. present here a method that finds appropriate architectures and hyperparameters for a wide range of drug design tasks and can achieve good performance without human intervention.

    • Yuquan Li
    • Chang-Yu Hsieh
    • Xiaojun Yao
  • Exoskeletons can assist movement in upper limb impairments to recover mobility and independence, but rigid or heavy exoskeletons can be impractical. Georgarakis and colleagues have developed a soft, tendon-driven device that assists shoulder movements and counteracts gravity to reduce muscle fatigue.

    • Anna-Maria Georgarakis
    • Michele Xiloyannis
    • Robert Riener
  • Robots usually learn to use tools from direct experience or from observing the use of a tool. While knowledge can be transferred between similar tools, novel and creative use of tools is challenging. Tee and colleagues present a method where skill transfer does not come from experience of using other tools but from using the robot’s own limbs.

    • Keng Peng Tee
    • Samuel Cheong
    • Gowrishankar Ganesh
  • While reinforcement learning can be a powerful tool for complex design tasks such as molecular design, training can be slow when problems are either too hard or too easy, as little is learned in these cases. Jeff Guo and colleagues provide a curriculum learning extension to the REINVENT de novo molecular design framework that provides problems of increasing difficulty over epochs such that the training process is more efficient.

    • Jeff Guo
    • Vendy Fialková
    • Atanas Patronov
  • B-cell receptors (BCRs) and their impact on B cells play a vital role in our immune system; however, the manner in which B cells are activated by BCRs are still poorly understood. Ze Zhang and colleagues present a graph-based method that connects BCR and single B-cell RNA sequencing data and identifies notable coupling between BCR and B-cell expression in COVID-19.

    • Ze Zhang
    • Woo Yong Chang
    • Tao Wang
  • Tactile sensing is needed for robots to physically interact with humans in daily living and in the workplace. A scientific challenge in robotics is how to simultaneously detect contact location and intensity. The authors describe a large-area sensing skin for robotic system applications, specifically for human–machine interactions.

    • Luca Massari
    • Giulia Fransvea
    • Calogero Maria Oddo
    Article Open Access
  • Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.

    • Longxi Zhou
    • Xianglin Meng
    • Xin Gao
    Article Open Access
  • Deep learning could be less energy intensive when implemented on spike-based neuromorphic chips. An approach inspired by a characteristic feature of biological neurons, the presence of slowly changing internal currents, is developed to emulate long short-term memory units in a sparse spiking regime for neuromorphic implementation.

    • Arjun Rao
    • Philipp Plank
    • Wolfgang Maass
  • Swarms of microrobots could eventually be used to deliver drugs to specific targets in the body, but the coordination of these swarms in complex environments is challenging. Yang and colleagues present a real-time autonomous distribution planning method based on deep learning that controls both the shape and position of the swarm, as well as the imaging system used for swarm navigation to cover longer distances.

    • Lidong Yang
    • Jialin Jiang
    • Li Zhang
  • With the availability of a vast and growing number of digital publications, machine reading and other knowledge mining tools, computational methods can be applied at scale to extract insights from the scientific literature. Belikov et al. develop a Bayesian method to mine the biomedical literature that identifies robust scientific findings which could improve the planning of further experiments and scientific investigation.

    • Alexander V. Belikov
    • Andrey Rzhetsky
    • James Evans
  • The invariant causal prediction (ICP) framework tries to determine the causal variables given an outcome variable, but considerable effort is needed to adapt existing ICP methods to the clinical domain. The authors propose an automated causal inference method that could potentially address the challenges of applying the ICP framework to complex clinical datasets.

    • Ji Q. Wu
    • Nanda Horeweg
    • Viktor H. Koelzer
    Article Open Access
  • The use of deep neural networks for the automated analysis of behavioural videos has emerged as a tool in neuroscience, medicine and psychology. Marks and colleagues present a pipeline capable of tracking and identifying animals, as well as classifying individual and interacting animal behaviour in video recordings and even in complex environments.

    • Markus Marks
    • Qiuhan Jin
    • Mehmet Fatih Yanik
  • Combinatorial optimization, the search for the minimum of an objective function within a finite but very large set of candidate solutions, finds many important and challenging applications in science and industry. A new graph neural network deep learning approach that incorporates concepts from statistical physics is used to develop a robust solver that can tackle a large class of NP-hard combinatorial optimization problems.

    • Martin J. A. Schuetz
    • J. Kyle Brubaker
    • Helmut G. Katzgraber
  • Accurate prediction of complex systems such as protein folding, weather forecasting and social dynamics is a core challenge in various disciplines. By fusing machine learning algorithms and classic equation-free methodologies, it is possible to reduce the computational effort of large-scale simulations by up to two orders of magnitude while maintaining the prediction accuracy of the full system dynamics.

    • Pantelis R. Vlachas
    • Georgios Arampatzis
    • Petros Koumoutsakos