Nature Machine Intelligence

Papers
(The H4-Index of Nature Machine Intelligence is 71. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-05-01 to 2024-05-01.)
ArticleCitations
An interpretable mortality prediction model for COVID-19 patients665
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators620
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans602
Shortcut learning in deep neural networks577
Secure, privacy-preserving and federated machine learning in medical imaging497
Drug discovery with explainable artificial intelligence432
Deep learning for tomographic image reconstruction246
Machine learning pipeline for battery state-of-health estimation245
AI for radiographic COVID-19 detection selects shortcuts over signal239
An open source machine learning framework for efficient and transparent systematic reviews236
Molecular contrastive learning of representations via graph neural networks199
Inverse design of nanoporous crystalline reticular materials with deep generative models177
Finding key players in complex networks through deep reinforcement learning176
Expanding functional protein sequence spaces using generative adversarial networks172
Geometry-enhanced molecular representation learning for property prediction165
End-to-end privacy preserving deep learning on multi-institutional medical imaging164
The carbon impact of artificial intelligence163
Ensemble deep learning in bioinformatics163
Causal inference and counterfactual prediction in machine learning for actionable healthcare155
Improved protein structure prediction by deep learning irrespective of co-evolution information142
Concept whitening for interpretable image recognition126
Development of metaverse for intelligent healthcare126
Mapping the space of chemical reactions using attention-based neural networks124
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks122
Enabling the future of colonoscopy with intelligent and autonomous magnetic manipulation119
Deep learning-based prediction of the T cell receptor–antigen binding specificity112
The rise of robots in surgical environments during COVID-19109
Advances, challenges and opportunities in creating data for trustworthy AI108
Geometric deep learning on molecular representations108
Database-independent molecular formula annotation using Gibbs sampling through ZODIAC104
Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion104
A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing102
Bioinspired acousto-magnetic microswarm robots with upstream motility102
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science101
Neural circuit policies enabling auditable autonomy101
Code-free deep learning for multi-modality medical image classification100
Dual use of artificial-intelligence-powered drug discovery97
Estimation of continuous valence and arousal levels from faces in naturalistic conditions97
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data96
Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network96
High-accuracy prostate cancer pathology using deep learning95
Towards a new generation of artificial intelligence in China95
Machine learning and computation-enabled intelligent sensor design95
Prediction of water stability of metal–organic frameworks using machine learning94
Making deep neural networks right for the right scientific reasons by interacting with their explanations93
A soft robot that adapts to environments through shape change93
Origami-inspired miniature manipulator for teleoperated microsurgery92
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms91
Machine learning and algorithmic fairness in public and population health89
Deep learning incorporating biologically inspired neural dynamics and in-memory computing89
Automating turbulence modelling by multi-agent reinforcement learning89
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors88
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning88
The transformational role of GPU computing and deep learning in drug discovery83
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis83
Artificial intelligence cooperation to support the global response to COVID-1981
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks81
Three types of incremental learning81
Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning80
Learning functional properties of proteins with language models78
Controllable protein design with language models78
A definition, benchmark and database of AI for social good initiatives76
Stable learning establishes some common ground between causal inference and machine learning75
Using online verification to prevent autonomous vehicles from causing accidents75
A soft thumb-sized vision-based sensor with accurate all-round force perception73
Improving performance of deep learning models with axiomatic attribution priors and expected gradients73
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations73
A versatile deep learning architecture for classification and label-free prediction of hyperspectral images72
Morphological and molecular breast cancer profiling through explainable machine learning72
An embedded ethics approach for AI development71
A geometric deep learning approach to predict binding conformations of bioactive molecules71
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