Nature computational science最新文献_第5页

Enabling efficient analysis of biobank-scale data with genotype representation graphs 通过基因型表示图实现生物库规模数据的有效分析。

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Nature computational science Pub Date : 2024-12-05 DOI: 10.1038/s43588-024-00739-9

Drew DeHaas, Ziqing Pan, Xinzhu Wei

{"title":"Enabling efficient analysis of biobank-scale data with genotype representation graphs","authors":"Drew DeHaas, Ziqing Pan, Xinzhu Wei","doi":"10.1038/s43588-024-00739-9","DOIUrl":"10.1038/s43588-024-00739-9","url":null,"abstract":"Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. However, encoding genetic data in existing tabular data structures and file formats has become costly and unsustainable. Here we introduce the genotype representation graph (GRG), a fully connected hierarchical data structure that losslessly encodes phased whole-genome polymorphisms. Exploiting variant-sharing across samples enables GRG to compress 200,000 UK Biobank phased human genomes to 5–26 gigabytes per chromosome, also enabling graph-traversal algorithms to reuse computed values in random access memory. Constructing and processing GRG files scales to a million whole genomes. Using allele frequencies and association effects as examples, we show that computation on GRG via graph traversal runs the fastest among all tested alternatives. GRG-based algorithms have the potential to increase the scalability and reduce the cost of analyzing large genomic datasets. The genotype representation graph (GRG) is a compact data structure that encodes 200,000 human genomes in just 5–26 gigabytes per chromosome. Computation on GRG via graph traversal greatly accelerates genome-wide analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"112-124"},"PeriodicalIF":12.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Teaching spin symmetry while learning neural network wave functions 在学习神经网络波函数的同时教授自旋对称。

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Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00727-z

Yongle Li, Yuhao Chen, Xiao He

引用次数: 0

Deep learning training dynamics analysis for single-cell data 单细胞数据的深度学习训练动态分析。

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Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00728-y

引用次数: 0

Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network 用深度神经网络求解多体Schrödinger方程的自旋对称强制解。

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Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00730-4

Zhe Li, Zixiang Lu, Ruichen Li, Xuelan Wen, Xiang Li, Liwei Wang, Ji Chen, Weiluo Ren

{"title":"Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network","authors":"Zhe Li, Zixiang Lu, Ruichen Li, Xuelan Wen, Xiang Li, Liwei Wang, Ji Chen, Weiluo Ren","doi":"10.1038/s43588-024-00730-4","DOIUrl":"10.1038/s43588-024-00730-4","url":null,"abstract":"The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases in which different spin states are nearly degenerate. Our approach also excels at spin–gap calculations, including the singlet–triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems. An efficient approach is developed to enforce spin symmetry for neural network wavefunctions when solving the many-body Schrödinger equation. This enables accurate and spin-pure simulations of both ground and excited states.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"910-919"},"PeriodicalIF":12.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpreting single-cell and spatial omics data using deep neural network training dynamics 利用深度神经网络训练动力学解释单细胞和空间组学数据。

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Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00721-5

Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan

{"title":"Interpreting single-cell and spatial omics data using deep neural network training dynamics","authors":"Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan","doi":"10.1038/s43588-024-00721-5","DOIUrl":"10.1038/s43588-024-00721-5","url":null,"abstract":"Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations on the basis of noisy, sparse and high-dimensional data. Here we developed Annotatability, a framework for identifying annotation mismatches and characterizing biological data structure by monitoring the dynamics and difficulty of training a deep neural network over such annotated data. Following this, we developed a signal-aware graph embedding method that enables downstream analysis of biological signals. This embedding captures cellular communities associated with target signals. Using Annotatability, we address key challenges in the interpretation of genomic data, demonstrated over eight single-cell RNA sequencing and spatial omics datasets, including identifying erroneous annotations and intermediate cell states, delineating developmental or disease trajectories, and capturing cellular heterogeneity. These results underscore the broad applicability of annotation-trainability analysis via Annotatability for unraveling cellular diversity and interpreting collective cell behaviors in health and disease. The Annotatability framework analyzes neural network training dynamics to interpret single-cell and spatial omics data. It identifies erroneous annotations and ambiguous cell states, infers trajectories from binary labels and enables signal-aware analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"941-954"},"PeriodicalIF":12.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00721-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive prediction and analysis of human protein essentiality based on a pretrained large language model. 基于预训练大型语言模型的人类蛋白质本质综合预测与分析。

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Nature computational science Pub Date : 2024-11-27 DOI: 10.1038/s43588-024-00733-1

Boming Kang, Rui Fan, Chunmei Cui, Qinghua Cui

引用次数: 0

Harnessing the power of DNA for computing 利用 DNA 的力量进行计算

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Nature computational science Pub Date : 2024-11-21 DOI: 10.1038/s43588-024-00742-0

引用次数: 0

Collective deliberation driven by AI 由人工智能驱动的集体审议。

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Nature computational science Pub Date : 2024-11-18 DOI: 10.1038/s43588-024-00736-y

Fernando Chirigati

引用次数: 0

Harnessing deep learning to build optimized ligands 利用深度学习构建优化配体。

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Nature computational science Pub Date : 2024-11-14 DOI: 10.1038/s43588-024-00725-1

Orestis A. Ntintas, Theodoros Daglis, Vassilis G. Gorgoulis

引用次数: 0

MassiveFold: unveiling AlphaFold’s hidden potential with optimized and parallelized massive sampling MassiveFold：通过优化和并行化的大规模采样挖掘 AlphaFold 隐藏的潜力。

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Nature computational science Pub Date : 2024-11-11 DOI: 10.1038/s43588-024-00714-4

Nessim Raouraoua, Claudio Mirabello, Thibaut Véry, Christophe Blanchet, Björn Wallner, Marc F. Lensink, Guillaume Brysbaert

引用次数: 0