Nature Machine Intelligence最新文献_第7页

Realistic morphology-preserving generative modelling of the brain 逼真的大脑形态保存生成模型

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-15 DOI: 10.1038/s42256-024-00864-0

Petru-Daniel Tudosiu, Walter H. L. Pinaya, Pedro Ferreira Da Costa, Jessica Dafflon, Ashay Patel, Pedro Borges, Virginia Fernandez, Mark S. Graham, Robert J. Gray, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

{"title":"Realistic morphology-preserving generative modelling of the brain","authors":"Petru-Daniel Tudosiu, Walter H. L. Pinaya, Pedro Ferreira Da Costa, Jessica Dafflon, Ashay Patel, Pedro Borges, Virginia Fernandez, Mark S. Graham, Robert J. Gray, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso","doi":"10.1038/s42256-024-00864-0","DOIUrl":"10.1038/s42256-024-00864-0","url":null,"abstract":"Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem. But are current generative models synthesizing morphologically correct samples? In this work we present a three-dimensional generative model of the human brain that is trained at the necessary scale to generate diverse, realistic-looking, high-resolution and morphologically preserving samples and conditioned on patient characteristics (for example, age and pathology). We show that the synthetic samples generated by the model preserve biological and disease phenotypes and are realistic enough to permit use downstream in well-established image analysis tools. While the proposed model has broad future applicability, such as anomaly detection and learning under limited data, its generative capabilities can be used to directly mitigate data scarcity, limited data availability and algorithmic fairness. Medical imaging research is limited by data availability. To address this challenge, Tudosiu and colleagues develop a 3D generative model of the human brain that can generate high-resolution morphologically correct brains conditioned on patient characteristics.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 7","pages":"811-819"},"PeriodicalIF":18.8,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00864-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-resolution real-space reconstruction of cryo-EM structures using a neural field network 利用神经场网络对低温电子显微镜结构进行高分辨率实空间重建

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-12 DOI: 10.1038/s42256-024-00870-2

Yue Huang, Chengguang Zhu, Xiaokang Yang, Manhua Liu

{"title":"High-resolution real-space reconstruction of cryo-EM structures using a neural field network","authors":"Yue Huang, Chengguang Zhu, Xiaokang Yang, Manhua Liu","doi":"10.1038/s42256-024-00870-2","DOIUrl":"10.1038/s42256-024-00870-2","url":null,"abstract":"The elucidation of three-dimensional (3D) structures is crucial for unravelling the protein function and illuminating mechanisms in structural biology. Cryogenic electron microscopy (cryo-EM) single-particle analysis provides direct measurements to determine the structures of macromolecules. However, the main challenge is reconstructing high-resolution 3D structures from extremely noisy and randomly oriented two-dimensional projection images. Most existing methods involve the optimization of multiple two-dimensional slices in the Fourier domain but ignore the anisotropy among these slices, thereby limiting the reconstruction of high-frequency structures. In this paper, we propose a cryo-EM neural field reconstruction network using 3D spatial-domain optimization that learns a directional isotropic representation of the cryo-EM structure by mapping the spatial coordinates to the corresponding density values. We qualitatively and quantitatively evaluate the cryo-EM neural field reconstruction network on four datasets. The cryo-EM neural field reconstruction network improves the directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolves the missing elements of SARS-CoV-2 in heterogeneous reconstruction. Elucidating three-dimensional structures is crucial for unravelling the macromolecule function in structural biology. This study presents a cryogenic electron microscopy neural field reconstruction network using real-space optimization, enhancing the resolution in cryogenic electron microscopy reconstruction.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 8","pages":"892-903"},"PeriodicalIF":18.8,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised learning of topological non-Abelian braiding in non-Hermitian bands 非ermitian 带中拓扑非阿贝尔辫状结构的无监督学习

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-12 DOI: 10.1038/s42256-024-00871-1

Yang Long, Haoran Xue, Baile Zhang

{"title":"Unsupervised learning of topological non-Abelian braiding in non-Hermitian bands","authors":"Yang Long, Haoran Xue, Baile Zhang","doi":"10.1038/s42256-024-00871-1","DOIUrl":"10.1038/s42256-024-00871-1","url":null,"abstract":"The topological classification of energy bands has laid the foundation for the discovery of various topological phases of matter in recent decades. While previous work focused on real-energy bands in Hermitian systems, recent studies have shifted attention to the intriguing topology of complex-energy, or non-Hermitian, bands, freeing them from the constraint of energy conservation. For example, the spectral winding of complex-energy bands can give rise to unique topological structures such as braids, holding substantial promise for advancing quantum computing. However, discussions of complex-energy braids have been predominantly limited to the Abelian braid group $${{mathbb{B}}}_{2}$$ owing to its relative simplicity. Identifying topological non-Abelian braiding remains challenging, as it lacks a universally applicable topological invariant for characterization. Here we present a machine learning algorithm for the unsupervised identification of non-Abelian braiding within multiple complex-energy bands. We demonstrate that the results are consistent with Artin’s well-known topological equivalence conditions in braiding. Inspired by these findings, we introduce a winding matrix as a topological invariant for characterizing braiding topology. The winding matrix also reveals the bulk-edge correspondence of non-Hermitian bands with non-Abelian braiding. Finally, we extend our approach to identify non-Abelian braiding topology in two-dimensional and three-dimensional exceptional semimetals and address the unknotting problem in an unsupervised manner. The topological classification of complex-energy bands has uncovered various topological phases beyond Hermitian systems. Long and colleagues exploit unsupervised learning to fully identify the non-Abelian braiding topology of non-Hermitian bands.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 8","pages":"904-910"},"PeriodicalIF":18.8,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An interpretable deep learning framework for genome-informed precision oncology 用于基因组信息精准肿瘤学的可解释深度学习框架

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-11 DOI: 10.1038/s42256-024-00866-y

Shuangxia Ren, Gregory F. Cooper, Lujia Chen, Xinghua Lu

{"title":"An interpretable deep learning framework for genome-informed precision oncology","authors":"Shuangxia Ren, Gregory F. Cooper, Lujia Chen, Xinghua Lu","doi":"10.1038/s42256-024-00866-y","DOIUrl":"10.1038/s42256-024-00866-y","url":null,"abstract":"Cancers result from aberrations in cellular signalling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumours. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: (1) a representation-learning component, which learns a representation of the cellular signalling systems when perturbed by SGAs and uses a biologically motivated and interpretable deep learning model, and (2) a drug-response prediction component, which predicts drug responses by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework notably improves the accuracy of predictions of drug responses compared to models using SGAs directly in cell lines. Moreover, our model performs well with real patient data. Importantly, our framework enables the prediction of responses to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs. Precision oncology requires analysis of genomic alterations in cancer cells. Ren et al. develop an interpretable artificial intelligence framework that transforms somatic genomic alterations into representations of cellular signalling systems and accurately predicts cells’ responses to anticancer drugs.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 8","pages":"864-875"},"PeriodicalIF":18.8,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shielding sensitive medical imaging data 屏蔽敏感的医学成像数据

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-11 DOI: 10.1038/s42256-024-00865-z

Gaoyang Liu, Chen Wang, Tian Xia

引用次数: 0

Lifelike agility and play in quadrupedal robots using reinforcement learning and generative pre-trained models 利用强化学习和生成预训练模型实现四足机器人逼真的敏捷性和游戏性

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-05 DOI: 10.1038/s42256-024-00861-3

Lei Han, Qingxu Zhu, Jiapeng Sheng, Chong Zhang, Tingguang Li, Yizheng Zhang, He Zhang, Yuzhen Liu, Cheng Zhou, Rui Zhao, Jie Li, Yufeng Zhang, Rui Wang, Wanchao Chi, Xiong Li, Yonghui Zhu, Lingzhu Xiang, Xiao Teng, Zhengyou Zhang

{"title":"Lifelike agility and play in quadrupedal robots using reinforcement learning and generative pre-trained models","authors":"Lei Han, Qingxu Zhu, Jiapeng Sheng, Chong Zhang, Tingguang Li, Yizheng Zhang, He Zhang, Yuzhen Liu, Cheng Zhou, Rui Zhao, Jie Li, Yufeng Zhang, Rui Wang, Wanchao Chi, Xiong Li, Yonghui Zhu, Lingzhu Xiang, Xiao Teng, Zhengyou Zhang","doi":"10.1038/s42256-024-00861-3","DOIUrl":"10.1038/s42256-024-00861-3","url":null,"abstract":"Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or reinforcement learning approaches. These methods usually rely on physical models or handcrafted rewards to accurately describe the specific system, rather than on a generalized understanding like animals do. Here we propose a hierarchical framework to construct primitive-, environmental- and strategic-level knowledge that are all pre-trainable, reusable and enrichable for legged robots. The primitive module summarizes knowledge from animal motion data, where, inspired by large pre-trained models in language and image understanding, we introduce deep generative models to produce motor control signals stimulating legged robots to act like real animals. Then, we shape various traversing capabilities at a higher level to align with the environment by reusing the primitive module. Finally, a strategic module is trained focusing on complex downstream tasks by reusing the knowledge from previous levels. We apply the trained hierarchical controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles and play in a designed challenging multi-agent chase tag game, where lifelike agility and strategy emerge in the robots. A key challenge in robotics is leveraging pre-training as a form of knowledge to generate movements. The authors propose a general learning framework for reusing pre-trained knowledge across different perception and task levels. The deployed robots exhibit lifelike agility and sophisticated game-playing strategies.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 7","pages":"787-798"},"PeriodicalIF":18.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular set representation learning 分子集表示学习

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-07-05 DOI: 10.1038/s42256-024-00856-0

Maria Boulougouri, Pierre Vandergheynst, Daniel Probst

{"title":"Molecular set representation learning","authors":"Maria Boulougouri, Pierre Vandergheynst, Daniel Probst","doi":"10.1038/s42256-024-00856-0","DOIUrl":"10.1038/s42256-024-00856-0","url":null,"abstract":"Computational representation of molecules can take many forms, including graphs, string encodings of graphs, binary vectors or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regression tasks using a wide range of machine learning models. However, existing models come with limitations, such as the requirement for clearly defined chemical bonds, which often do not represent the true underlying nature of a molecule. Here we propose a framework for molecular machine learning tasks based on set representation learning. We show that learning on sets of atom invariants alone reaches the performance of state-of-the-art graph-based models on the most-used chemical benchmark datasets and that introducing a set representation layer into graph neural networks can surpass the performance of established methods in the domains of chemistry, biology and material science. We introduce specialized set representation-based neural network architectures for reaction-yield and protein–ligand binding-affinity prediction. Overall, we show that the technique we denote molecular set representation learning is both an alternative and an extension to graph neural network architectures for machine learning tasks on molecules, molecule complexes and chemical reactions. Machine learning methods for molecule predictions use various representations of molecules such as in the form of strings or graphs. As an extension of graph representation learning, Probst and colleagues propose to represent a molecule as a set of atoms, to better capture the underlying chemical nature, and demonstrate improved performance in a range of machine learning tasks.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 7","pages":"754-763"},"PeriodicalIF":18.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00856-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual odometry with neuromorphic resonator networks 利用神经形态共振网络进行视觉里程测量

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-06-27 DOI: 10.1038/s42256-024-00846-2

Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya

{"title":"Visual odometry with neuromorphic resonator networks","authors":"Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya","doi":"10.1038/s42256-024-00846-2","DOIUrl":"10.1038/s42256-024-00846-2","url":null,"abstract":"Visual odometry (VO) is a method used to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate errors, such as inertial sensors or wheel encoders, VO is not compromised by drift. However, image-based VO is computationally demanding, limiting its application in use cases with low-latency, low-memory and low-energy requirements. Neuromorphic hardware offers low-power solutions to many vision and artificial intelligence problems, but designing such solutions is complicated and often has to be assembled from scratch. Here we propose the use of vector symbolic architecture (VSA) as an abstraction layer to design algorithms compatible with neuromorphic hardware. Building from a VSA model for scene analysis, described in our companion paper, we present a modular neuromorphic algorithm that achieves state-of-the-art performance on two-dimensional VO tasks. Specifically, the proposed algorithm stores and updates a working memory of the presented visual environment. Based on this working memory, a resonator network estimates the changing location and orientation of the camera. We experimentally validate the neuromorphic VSA-based approach to VO with two benchmarks: one based on an event-camera dataset and the other in a dynamic scene with a robotic task. Visual odometry, or self-motion estimation, is a fundamental task in robotics. Renner, Supic and colleagues introduce a neuromorphic algorithm for visual odometry that leverages hyperdimensional computing and hierarchical resonators. The approach estimates a robot’s motion from event-based vision, a step towards low-power machine vision for robotics.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 6","pages":"653-663"},"PeriodicalIF":18.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning for micro- and nanorobots 微型和纳米机器人的机器学习

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-06-27 DOI: 10.1038/s42256-024-00859-x

Lidong Yang, Jialin Jiang, Fengtong Ji, Yangmin Li, Kai-Leung Yung, Antoine Ferreira, Li Zhang

{"title":"Machine learning for micro- and nanorobots","authors":"Lidong Yang, Jialin Jiang, Fengtong Ji, Yangmin Li, Kai-Leung Yung, Antoine Ferreira, Li Zhang","doi":"10.1038/s42256-024-00859-x","DOIUrl":"10.1038/s42256-024-00859-x","url":null,"abstract":"Machine learning (ML) has revolutionized robotics by enhancing perception, adaptability, decision-making and more, enabling robots to work in complex scenarios beyond the capabilities of traditional approaches. However, the downsizing of robots to micro- and nanoscales introduces new challenges. For example, complexities in the actuation and locomotion of micro- and nanorobots defy traditional modelling methods, while control and navigation are complicated by strong environmental disruptions, and tracking in vivo encounters substantial noise interference. Recently, ML has also been shown to offer a promising avenue to tackle these complexities. Here we discuss how ML advances many crucial aspects of micro- and nanorobots, that is, in their design, actuation, locomotion, planning, tracking and navigation. Any application that can benefit from these fundamental advancements will be a potential beneficiary of this field, including micromanipulation, targeted delivery and therapy, bio-sensing, diagnosis and so on. This Review aims to provide an accessible and comprehensive survey for readers to quickly appreciate recent exciting accomplishments in ML for micro- and nanorobots. We also discuss potential issues and prospects of this burgeoning research direction. We hope this Review can foster interdisciplinary collaborations across robotics, computer science, material science and allied disciplines, to develop ML techniques that surmount fundamental challenges and further expand the application horizons of micro- and nanorobotics in biomedicine. Machine learning approaches in micro- and nanorobotics promise to overcome challenges encountered by applying traditional control methods at the microscopic scale. Lidong Yang et al. review this emerging area in robotics and discuss machine learning developments in design, actuation, locomotion, planning, tracking and navigation of microrobots.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 6","pages":"605-618"},"PeriodicalIF":18.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Will generative AI transform robotics? 生成式人工智能会改变机器人技术吗？

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-06-27 DOI: 10.1038/s42256-024-00862-2

引用次数: 0