Research articles

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  • 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
  • Multiplex immunofluorescence imaging can provide a wealth of data compared to immunohistochemical staining, which is cheaper and more widely available. Ghahremani et al. present DeepLIIF, a GAN-based cell segmentation and classification approach, which is trained on co-registered images of these two modalities to provide the insights from the more data-rich muliplex data from simpler IHC images.

    • Parmida Ghahremani
    • Yanyun Li
    • Saad Nadeem
  • Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra.

    • Tom Altenburg
    • Sven H. Giese
    • Bernhard Y. Renard
    Article Open Access
  • To perform electronic structure calculations in quantum chemistry systems, methods are needed that are both accurate and scalable as the size of the molecule of interest increases. Barrett and colleagues employ an autoregressive neural-network ansatz that allows them to study larger molecules than previously attempted with neural-network quantum state approaches.

    • Thomas D. Barrett
    • Aleksei Malyshev
    • A. I. Lvovsky
  • Deep learning methods have in recent years shown promising results in characterizing proteins and extracting complex sequence–structure–function relationships. This Analysis describes a benchmarking study to compare the performances and advantages of recent deep learning approaches in a range of protein prediction tasks.

    • Serbulent Unsal
    • Heval Atas
    • Tunca Doğan
  • Knowledge of the wide array of epigenomic signals provides biological insight into the state of a give cell type, but it is infeasible to experimentally characterize all possible types of epigenomic signal in the multitude of cell types in the human body. The authors present Ocelot, a machine learning approach for imputing cell-type-specific epigenomic signals along the genome.

    • Hongyang Li
    • Yuanfang Guan