Computational science articles within Nature Communications

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  • Article
    | Open Access

    Accurate prediction of peptidic hydrogels could prove useful for diverse biomedical applications. Here, the authors develop a “human-in-the-loop” approach that integrates coarse-grained molecular dynamics, machine learning, and experimentation to design natural peptide hydrogels.

    • Tengyan Xu
    • , Jiaqi Wang
    •  & Huaimin Wang

  • Article
    | Open Access

    The authors propose a confocal complemented signal-object collaborative regularization method for non-line-of-sight (NLOS) imaging without specific requirements on the spatial pattern of measurement points. The method extends the application range of NLOS imaging.

    • Xintong Liu
    • , Jianyu Wang
    •  & Lingyun Qiu

  • Article
    | Open Access

    Recent experiments reveal undetermined crystalline phases near the melting minimum region in lithium. Here, the authors use a crystal structure search method combined with machine learning to explore the energy landscape of lithium and predict complex crystal structures.

    • Xiaoyang Wang
    • , Zhenyu Wang
    •  & Yanming Ma

  • Article
    | Open Access

    In quantum technologies, scalable ways to characterise errors in quantum hardware are highly needed. Here, the authors propose an approximate version of quantum process tomography based on tensor network representations of the processes and data-driven optimisation.

    • Giacomo Torlai
    • , Christopher J. Wood
    •  & Leandro Aolita

  • Article
    | Open Access

    Carbon (12C) nucleus has interesting characteristics including the existence of the Hoyle state. Here the authors discuss the structure of the nuclear states of 12C by using nuclear lattice effective field theory.

    • Shihang Shen
    • , Serdar Elhatisari
    •  & Ulf-G. Meißner

  • Article
    | Open Access

    Multimodal biological data is challenging to analyze. Here, the authors develop UnitedNet, an explainable deep neural network for analyzing single-cell multimodal biological data and estimating relationships between gene expression and other modalities with cell-type specificity.

    • Xin Tang
    • , Jiawei Zhang
    •  & Jia Liu

  • Article
    | Open Access

    Evidence suggests that increased consumption of ultra-processed food has adverse health implications, however, it remains difficult to classify processed food. Here, the authors introduce FPro, a machine learning-based score predicting the degree of food processing.

    • Giulia Menichetti
    • , Babak Ravandi
    •  & Albert-László Barabási

  • Article
    | Open Access

    Accurately annotating cell types is a fundamental step in single-cell omics data analysis. Here, the authors develop a computational method called Cellcano based on a two-round supervised learning algorithm to identify cell types for scATAC-seq data and perform benchmarking to demonstrate its accuracy, robustness and computational efficiency.

    • Wenjing Ma
    • , Jiaying Lu
    •  & Hao Wu

  • Article
    | Open Access

    The biological plausibility of backpropagation and its relationship with synaptic plasticity remain open questions. The authors propose a meta-learning approach to discover interpretable plasticity rules to train neural networks under biological constraints. The meta-learned rules boost the learning efficiency via bio-inspired synaptic plasticity.

    • Navid Shervani-Tabar
    •  & Robert Rosenbaum

  • Article
    | Open Access

    Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. Here the authors benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches and suggest several high-performance methods under different conditions based on simulation and real data analyses.

    • Hai C. T. Nguyen
    • , Bukyung Baik
    •  & Dougu Nam

  • Article
    | Open Access

    Visualization of large complex networks is challenging with limitations for the network size and depicting specific structures. The authors propose a Graph Neural Network based algorithm with improved speed and the quality of graph layouts, which allows for fast and informative visualization of large networks.

    • Csaba Both
    • , Nima Dehmamy
    •  & Albert-László Barabási

  • Article
    | Open Access

    Learning analytical models from noisy data remains challenging and depends essentially on the noise level. The authors analyze the transition of the model-learning problem from a low-noise phase to a phase where noise is too high for the underlying model to be learned by any method, and estimate upper bounds for the transition noise.

    • Oscar Fajardo-Fontiveros
    • , Ignasi Reichardt
    •  & Roger Guimerà

  • Article
    | Open Access

    The full potential of single-cell RNA-sequencing applied to precision medicine has yet to be reached. Here, we propose a drug recommendation system ASGARD, which predicts drugs by considering cell clusters to address the intercellular heterogeneity within each patient.

    • Bing He
    • , Yao Xiao
    •  & Lana X. Garmire

  • Article
    | Open Access

    Finding the ground states of spin glasses relevant for disordered magnets and many other physical systems is computationally challenging. The authors propose here a deep reinforcement learning framework for calculating the ground states, which can be trained on small-scale spin glass instances and then applied to arbitrarily large ones.

    • Changjun Fan
    • , Mutian Shen
    •  & Yang-Yu Liu

  • Article
    | Open Access

    Existing genomic data analysis methods tend to not take full advantage of underlying biological characteristics. Here, the authors leverage the inherent interactions of scRNA-seq data and develop a cartography strategy to contrive the data into a spatially configured genomap for accurate deep pattern discovery.

    • Md Tauhidul Islam
    •  & Lei Xing

  • Article
    | Open Access

    The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.

    • Albert Musaelian
    • , Simon Batzner
    •  & Boris Kozinsky

  • Article
    | Open Access

    Feedback control applied to mechanical resonators can lead to the formation of various complex dynamic behaviors. Here the authors demonstrate flexible and controllable switching between dynamical structures in the response of harmonically driven micro-mechanical resonators.

    • Axel M. Eriksson
    • , Oriel Shoshani
    •  & David A. Czaplewski

  • Article
    | Open Access

    Traditional learning procedures for artificial intelligence rely on digital methods not suitable for physical hardware. Here, Nakajima et al. demonstrate gradient-free physical deep learning by augmenting a biologically inspired algorithm, accelerating the computation speed on optoelectronic hardware.

    • Mitsumasa Nakajima
    • , Katsuma Inoue
    •  & Kohei Nakajima

  • Article
    | Open Access

    This study presents the first rapid seismic multi-hazard and impact estimation system integrating advanced causal inference and remote sensing techniques, which jointly estimates regional-scale and high-resolution maps of seismic multi-hazards and building damage from InSAR imageries.

    • Susu Xu
    • , Joshua Dimasaka
    •  & Hae Young Noh

  • Article
    | Open Access

    Artificial Intelligence has achieved success in a variety of single-player or competitive two-player games with no communication between players. Here, the authors propose an approach where Artificial Intelligence agents have ability to negotiate and form agreements, playing the board game Diplomacy.

    • János Kramár
    • , Tom Eccles
    •  & Yoram Bachrach

  • Article
    | Open Access

    Engineering quantum states requires precise manipulations at the atomic level. Here, the authors use deep reinforcement learning to manipulate Ag adatoms on Ag surfaces, which combined with path planning algorithms enables autonomous atomic assembly.

    • I-Ju Chen
    • , Markus Aapro
    •  & Adam S. Foster

  • Article
    | Open Access

    Systemic risk and bank bailout approaches have been the source of discussions on scientific, financial and governmental forums. An artificial intelligence technique is proposed to inform equitable bailout decisions that minimise taxpayers’ losses.

    • Daniele Petrone
    • , Neofytos Rodosthenous
    •  & Vito Latora

  • Article
    | Open Access

    Traditional bulk sequencing data lack information about cell-type-specific gene expression. Here, the authors develop a Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq, and apply it to analyze the cell type fractions and cell-type-specific gene expression in clinical data.

    • Yanshuo Chen
    • , Yixuan Wang
    •  & Yu Li

  • Article
    | Open Access

    Safe clinical deployment of deep learning models for digital pathology requires reliable estimates of predictive uncertainty. Here the authors describe an algorithm for quantifying whole-slide image uncertainty, demonstrating their approach with models trained to distinguish lung cancer subtypes.

    • James M. Dolezal
    • , Andrew Srisuwananukorn
    •  & Alexander T. Pearson

  • Article
    | Open Access

    The development of machine learning systems has to ensure their robustness and reliability. The authors introduce a framework that defines a principled process of machine learning system formation, from research to production, for various domains and data scenarios.

    • Alexander Lavin
    • , Ciarán M. Gilligan-Lee
    •  & Yarin Gal

  • Article
    | Open Access

    Improving the image depth perception of holograms while maintaining high image quality is a current challenge. Here the authors propose an efficient solution relying on a multi-plane hologram technique that reconstruct different blurred images and sharply focused images depending on a propagation distance.

    • Daeho Yang
    • , Wontaek Seo
    •  & Hong-Seok Lee

  • Article
    | Open Access

    Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

    • Narmin Ghaffari Laleh
    • , Daniel Truhn
    •  & Jakob Nikolas Kather

  • Article
    | Open Access

    Increasing the capacity of existing lines or adding new lines in power grids may, counterintuitively, reduce the system performance and promote blackouts. The authors propose an approach for prediction of edges that lower system performance and defining potential constrains for grid extensions.

    • Benjamin Schäfer
    • , Thiemo Pesch
    •  & Marc Timme

  • Article
    | Open Access

    Can AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on hand-coded physics.

    • Lasse Espeholt
    • , Shreya Agrawal
    •  & Nal Kalchbrenner

  • Article
    | Open Access

    3D printing is prone to errors and continuous monitoring and real-time correction during processing remains a significant challenge limiting its applied potential. Here, authors train a neural network to detect and correct diverse errors in real time across many geometries, materials and even printing setups.

    • Douglas A. J. Brion
    •  & Sebastian W. Pattinson

  • Article
    | Open Access

    Series of machine learning models, relevant for tasks in biology, medicine, and finance, usually involve complex feature attribution techniques. The authors introduce a tractable method to compute local feature attributions for a series of machine learning models inspired by connections to the Shapley value.

    • Hugh Chen
    • , Scott M. Lundberg
    •  & Su-In Lee

  • Article
    | Open Access

    Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Here the authors apply some of the latest advances in natural language processing, generative Transformers, to train ProtGPT2, a language model that explores unseen regions of the protein space while designing proteins with nature-like properties.

    • Noelia Ferruz
    • , Steffen Schmidt
    •  & Birte Höcker

  • Article
    | Open Access

    Designing energy-efficient computing solution for the implementation of AI algorithms in edge devices remains a challenge. Yang et al. proposes a decentralized brain-inspired computing method enabling multiple edge devices to collaboratively train a global model without a fixed central coordinator.

    • Helin Yang
    • , Kwok-Yan Lam
    •  & H. Vincent Poor

  • Article
    | Open Access

    As mass quarantines, absences due to sickness, or other shocks thin out patient-physician networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, the authors propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model.

    • Michaela Kaleta
    • , Jana Lasser
    •  & Peter Klimek

  • Article
    | Open Access

    A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

    • Honglei Ren
    • , Benjamin L. Walker
    •  & Qing Nie

  • Article
    | Open Access

    Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

    • Sergey P. Primakov
    • , Abdalla Ibrahim
    •  & Philippe Lambin

  • Article
    | Open Access

    Machine learning tools allow to extract dynamical systems from data, however this problem remains challenging for networks and systems of interacting agents. The authors introduce an approach to learn a predictive model for the dynamics of coupled agents in the form of partial differential equations using emergent spatial embeddings.

    • Felix P. Kemeth
    • , Tom Bertalan
    •  & Ioannis G. Kevrekidis

  • Article
    | Open Access

    Scattering of electrons from defects and boundaries in mesoscopic samples is encoded in quantum interference patterns of magneto-conductance, but these patterns are difficult to interpret. Here the authors use machine learning to reconstruct electron wavefunction intensities and sample geometry from magneto-conductance data.

    • Shunsuke Daimon
    • , Kakeru Tsunekawa
    •  & Eiji Saitoh

  • Article
    | Open Access

    Understanding the transport of the particles and fuel in the fusion plasma is fundamentally important. Here the authors report a cross-link interaction between electron- and ion-scale turbulences in plasma in terms of trapped electron mode and electron temperature gradient modes and their implication to fusion plasma.

    • Shinya Maeyama
    • , Tomo-Hiko Watanabe
    •  & Akihiro Ishizawa

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