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| Open AccessAtlas-scale single-cell multi-sample multi-condition data integration using scMerge2
Recent advances in multi-condition single-cell multi-cohort studies enable exploration of diverse cell states. Here, authors present scMerge2, an algorithm that allows integration of a large COVID-19 data collection with over five million cells to uncover distinct signatures of disease progression.
- Yingxin Lin
- , Yue Cao
- & Jean Y. H. Yang
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Article
| Open AccessFinding defects in glasses through machine learning
Rare quantum tunneling two-level systems are known to govern the glass physics at low temperatures, but it remains challenging to detect them in simulations. Ciarella et al. show a machine learning approach to efficiently identify the structural defects, allowing to predict the quantum splitting.
- Simone Ciarella
- , Dmytro Khomenko
- & Francesco Zamponi
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Article
| Open AccessThe effect of environmental information on evolution of cooperation in stochastic games
In stochastic games, there is a feedback loop between a group’s social behaviors and its environment. Kleshnina et al. show that groups are often more cooperative when they know the exact state of their environment, although there are also intriguing cases when ignorance is beneficial.
- Maria Kleshnina
- , Christian Hilbe
- & Martin A. Nowak
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Article
| Open AccesspolyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
The polymer universe is gigantic. Searching this space effectively requires ultrafast high-fidelity property prediction methods. Here, the authors present a chemical language model that can probe this space at unprecedented speed and accuracy.
- Christopher Kuenneth
- & Rampi Ramprasad
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Article
| Open AccessnnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
The identification of top spatially variable genes is a key step in the analysis of spatially-resolved transcriptomics data. Here, the authors develop a scalable method based on nearest-neighbor Gaussian processes and evaluate performance compared to existing and baseline methods.
- Lukas M. Weber
- , Arkajyoti Saha
- & Stephanie C. Hicks
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Article
| Open AccessLeveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry
Cell location information is important for understanding how tissue is spatially organized. Here, the authors develop CeLEry, a machine learning method that aims to recover cell locations for single-cell RNA-seq data by leveraging information learned from spatial transcriptomics.
- Qihuang Zhang
- , Shunzhou Jiang
- & Mingyao Li
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Article
| Open AccessOut-of-distribution generalization for learning quantum dynamics
Generalization - that is, the ability to extrapolate from training data to unseen data - is fundamental in machine learning, and thus also for quantum ML. Here, the authors show that QML algorithms are able to generalise the training they had on a specific distribution and learn over different distributions.
- Matthias C. Caro
- , Hsin-Yuan Huang
- & Zoë Holmes
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Article
| Open AccessAccelerating the prediction and discovery of peptide hydrogels with human-in-the-loop
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
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Article
| Open AccessCAJAL enables analysis and integration of single-cell morphological data using metric geometry
Cell morphology is one of the most described phenotypes in biology, yet systematic quantification and classification of morphology remains limited. Here, the authors present a computational approach for cell morphometry and multi-modal analysis based on concepts from metric geometry.
- Kiya W. Govek
- , Patrick Nicodemus
- & Pablo G. Camara
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Article
| Open AccessWaves traveling over a map of visual space can ignite short-term predictions of sensory input
Waves of neural activity travel across single regions in the visual cortex, but their computational role is unclear. Here, the authors present a neural network model demonstrating that waves traveling over retinotopic maps can enable short-term predictions of future inputs.
- Gabriel B. Benigno
- , Roberto C. Budzinski
- & Lyle Muller
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Article
| Open AccessInflationary theory of branching morphogenesis in the mouse salivary gland
The authors show that the ramified ductal network of the mouse salivary gland develops from a set of simple probabilistic rules, where ductal elongation and branching are driven by the persistent expansion of the surrounding tissue.
- Ignacio Bordeu
- , Lemonia Chatzeli
- & Benjamin D. Simons
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Article
| Open AccessNon-line-of-sight imaging with arbitrary illumination and detection pattern
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
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Article
| Open AccessCASPI: collaborative photon processing for active single-photon imaging
The sparse, noisy, and distorted raw photon data captured by single-photon cameras make it difficult to estimate scene properties under challenging illumination conditions. Here, the authors present Collaborative photon processing for Active Single-Photon Imaging (CASPI), a technology-agnostic, application-agnostic, and training-free photon processing pipeline for high-resolution single-photon cameras.
- Jongho Lee
- , Atul Ingle
- & Mohit Gupta
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Article
| Open AccessData-driven direct diagnosis of Li-ion batteries connected to photovoltaics
Li-ion batteries are used to store energy harvested from photovoltaics. However, battery use is sporadic and standard diagnostic methods cannot be applied. Here, the authors propose a methodology for diagnosing photovoltaics-connected Li-ion batteries that use trained machine learning algorithms.
- Matthieu Dubarry
- , Nahuel Costa
- & Dax Matthews
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Article
| Open AccessRetention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network
Chromatographic enantioseparation requires tedious trials to find proper experimental conditions. Here, the authors construct a deep learning model to predict retention times of chiral molecules and obtain the separation probability under given conditions.
- Hao Xu
- , Jinglong Lin
- & Fanyang Mo
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Article
| Open AccessEvidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics
Amid the COVID-19 pandemic, accurate hospitalization predictions are vital. Here, the authors show that a deep learning model based on statistical mechanics is able to forecast hospitalizations, supporting targeted vaccination efforts.
- Junyi Gao
- , Joerg Heintz
- & Jimeng Sun
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Article
| Open AccessInference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Here, the authors present single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer cell type-specific GRN dynamics from scRNA-seq and scATAC-seq datasets collected for diverse cell fate specification trajectories.
- Shilu Zhang
- , Saptarshi Pyne
- & Sushmita Roy
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Article
| Open AccessRetrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing
Retrosynthesis prediction is a fundamental problem in organic synthesis. Here, inspired by simplified arrow-pushing reaction mechanisms, the authors develop a graph-to-edits framework, Graph2Edits, based on graph neural network for retrosynthesis prediction.
- Weihe Zhong
- , Ziduo Yang
- & Calvin Yu-Chian Chen
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Article
| Open AccessDecentralized federated learning through proxy model sharing
Federated learning enables multi-institutional collaborations on decentralized data with improved privacy protection. Here, authors propose a new scheme for decentralized federated learning with much less communication overhead and stronger privacy.
- Shivam Kalra
- , Junfeng Wen
- & H. R. Tizhoosh
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Article
| Open AccessSpatially-optimized urban greening for reduction of population exposure to land surface temperature extremes
This study uses earth observation data and proposes a method to evaluate and optimize the increment of urban greening to reduce the population exposure to extreme land surface temperatures in cities.
- Emanuele Massaro
- , Rossano Schifanella
- & Gregory Duveiller
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Article
| Open AccessData-driven prediction of complex crystal structures of dense lithium
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
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Article
| Open AccessQuantum process tomography with unsupervised learning and tensor networks
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
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Article
| Open AccessGeneral framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
Fundamental symmetries are crucial to the deep-learning modeling of physical systems. Here the authors use equivariant neural networks preserving the Euclidean symmetries to accelerate electronic structure calculations by orders of magnitude keeping sub-meV accuracy.
- Xiaoxun Gong
- , He Li
- & Yong Xu
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Article
| Open AccessEmergent geometry and duality in the carbon nucleus
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
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Article
| Open AccessOptical neural network via loose neuron array and functional learning
Here the authors have realized a programmable incoherent optical neural network that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space.
- Yuchi Huo
- , Hujun Bao
- & Sung-Eui Yoon
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Article
| Open AccessExplainable multi-task learning for multi-modality biological data analysis
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
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Article
| Open AccessBifurcation behaviors shape how continuous physical dynamics solves discrete Ising optimization
Physical and physics-inspired computation is emerging as a new paradigm for tackling hard optimization problems. In this work, the authors establish rigorous mathematical conditions together with new design principles for physical as well as simulated dynamical systems to solve general Ising models.
- Juntao Wang
- , Daniel Ebler
- & Jie Sun
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Article
| Open AccessCross-modal autoencoder framework learns holistic representations of cardiovascular state
A challenge in diagnostics is integrating different data modalities to characterize physiological state. Here, the authors show, using the heart as a model system, that cross-modal autoencoders can integrate and translate modalities to improve diagnostics and identify associated genetic variants.
- Adityanarayanan Radhakrishnan
- , Sam F. Friedman
- & Caroline Uhler
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Article
| Open AccessThe most at-risk regions in the world for high-impact heatwaves
The global risk of record-breaking heatwaves is assessed, with the most at-risk regions identified. It is shown that record-smashing events that currently appear implausible could happen anywhere as a result of climate change.
- Vikki Thompson
- , Dann Mitchell
- & Julia M. Slingo
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Article
| Open AccessMachine learning prediction of the degree of food processing
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
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Article
| Open AccessActive learning-assisted neutron spectroscopy with log-Gaussian processes
Neutron scattering experiments are important for studying materials properties. Here, the authors present a probabilistic active learning approach for neutron spectroscopy with three-axes spectrometers and demonstrate optimization of beam time use by favoring informative regions of signal.
- Mario Teixeira Parente
- , Georg Brandl
- & Astrid Schneidewind
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Article
| Open AccessComplex computation from developmental priors
Neuroscience has long inspired AI, however the neuroevolutionary search that produces sophisticated behaviors has not been systematized. This paper defines neurodevelopmental ML as a discovery process for structures that promote complex computations.
- Dániel L. Barabási
- , Taliesin Beynon
- & Nicolas Perez-Nieves
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Article
| Open AccessRandom fractal-enabled physical unclonable functions with dynamic AI authentication
In order to be used on a large scale, unclonable tags for anti-counterfeiting should allow mass production at low cost, as well as fast and easy authentication. Here, the authors show how to use one-step annealing of gold films to quickly realize robust tags with high capacity, allowing fast deep-learning based authentication via smartphone readout.
- Ningfei Sun
- , Ziyu Chen
- & Qian Liu
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Article
| Open AccessCombining data and theory for derivable scientific discovery with AI-Descartes
Automatic extraction of consistent governing laws from data is a challenging problem. The authors propose a method that takes as input experimental data and background theory and combines symbolic regression with logical reasoning to obtain scientifically meaningful symbolic formulas.
- Cristina Cornelio
- , Sanjeeb Dash
- & Lior Horesh
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Article
| Open AccessWidespread global disparities between modelled and observed mid-depth ocean currents
Analysis of big Argo data reveals that model representation of global ocean circulation near 1000-m depth is substantially compromised by inaccuracies. Only 3.8% of the mid-depth ocean circulation can be considered accurately modelled.
- Fenzhen Su
- , Rong Fan
- & Fei Chai
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Article
| Open AccessAutomated optimisation of solubility and conformational stability of antibodies and proteins
Antibodies find key applications in research, diagnostics, and therapeutics, but their development can be impeded by poor stability or solubility. Here the authors developed a computational strategy that enables antibody optimisation, without affecting functionality.
- Angelo Rosace
- , Anja Bennett
- & Pietro Sormanni
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Article
| Open AccessAtomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3
Oxide solid electrolytes generally suffer from high grain boundary resistance. Here, the authors use advanced electron microscopy, along with an active learning moment tensor potential, to reveal the atomic-scale origin of low grain-boundary resistance in Li0.375Sr0.4375Ta0.75Zr0.25O3.
- Tom Lee
- , Ji Qi
- & Xiaoqing Pan
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Article
| Open AccessCellcano: supervised cell type identification for single cell ATAC-seq data
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
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Article
| Open AccessMeta-learning biologically plausible plasticity rules with random feedback pathways
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
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Perspective
| Open AccessCatalyzing next-generation Artificial Intelligence through NeuroAI
One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.
- Anthony Zador
- , Sean Escola
- & Doris Tsao
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Article
| Open AccessA comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics
This study comprehensively benchmarks 18 state-of-the-art methods for cellular deconvolution of spatial transcriptomics and provide decision-tree-style guidelines and recommendations for method selection.
- Haoyang Li
- , Juexiao Zhou
- & Xin Gao
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Article
| Open AccessBenchmarking integration of single-cell differential expression
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
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Article
| Open AccessAccelerating network layouts using graph neural networks
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
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Article
| Open AccessSpatial immunization to abate disease spreading in transportation hubs
Efficient spatial targeting of interventions could reduce the spread of infections in transportation hubs. Here, the authors assess the optimal locations to target in Heathrow airport using disease transmission models informed by a contact network based on anonymised location data from 200,000 individuals.
- Mattia Mazzoli
- , Riccardo Gallotti
- & José J. Ramasco
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Article
| Open AccessMultistability, intermittency, and hybrid transitions in social contagion models on hypergraphs
Social interactions often occur in groups of individuals, which can be mathematically represented as hypergraphs. In this study, the authors analyze the appearance of multistability, intermittency, and hybrid phase transitions in social contagion models on hypergraphs.
- Guilherme Ferraz de Arruda
- , Giovanni Petri
- & Yamir Moreno
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Article
| Open AccessThe dynamic nature of percolation on networks with triadic interactions
Triadic interactions are higher-order interactions relevant to many real complex systems. The authors develop a percolation theory for networks with triadic interactions and identify basic mechanisms for observing dynamical changes of the giant component such as the ones occurring in neuronal and climate networks.
- Hanlin Sun
- , Filippo Radicchi
- & Ginestra Bianconi
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Article
| Open AccessA variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data
The inference of clonal architectures in cancer using single-cell RNA-seq data remains challenging. Here, the authors develop SCEVAN, a variational algorithm for copy number-based clonal structure inference in single-cell RNA-seq data that can characterise evolution and heterogeneity in the tumour and the microenvironment.
- Antonio De Falco
- , Francesca Caruso
- & Michele Ceccarelli
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Article
| Open AccessComplex-tensor theory of simple smectics
As lamellar materials, smectics exhibit both liquid and solid characteristics, making them difficult to model at the mesoscale. Paget et al. propose a complex tensor order parameter that reflects the smectic symmetries, capable of describing complex defects including dislocations and disclinations.
- Jack Paget
- , Marco G. Mazza
- & Tyler N. Shendruk
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Article
| Open AccessFundamental limits to learning closed-form mathematical models from data
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