Nature computational science最新文献_第7页

Gaining biological insights through supervised data visualization 通过监督数据可视化获得生物学见解。

IF 20.3

Nature computational science Pub Date : 2026-06-30 DOI: 10.1038/s43588-026-00999-7

Jake S. Rhodes, Adrien Aumon, Sacha Morin, Marc Girard, Catherine Larochelle, Elsa Brunet-Ratnasingham, Amélie Pagliuzza, Lorie Marchitto, Wei Zhang, Adele Cutler, François Grand’Maison, Anhong Zhou, Andrés Finzi, Nicolas Chomont, Daniel E. Kaufmann, Stephanie Zandee, Alexandre Prat, Guy Wolf, Kevin R. Moon

{"title":"Gaining biological insights through supervised data visualization","authors":"Jake S. Rhodes, Adrien Aumon, Sacha Morin, Marc Girard, Catherine Larochelle, Elsa Brunet-Ratnasingham, Amélie Pagliuzza, Lorie Marchitto, Wei Zhang, Adele Cutler, François Grand’Maison, Anhong Zhou, Andrés Finzi, Nicolas Chomont, Daniel E. Kaufmann, Stephanie Zandee, Alexandre Prat, Guy Wolf, Kevin R. Moon","doi":"10.1038/s43588-026-00999-7","DOIUrl":"10.1038/s43588-026-00999-7","url":null,"abstract":"Dimensionality-reduction-based visualization is essential for interpreting complex biological data. Yet, unsupervised methods such as t-distributed stochastic neighbor embedding, Uniform Manifold Approximation and Projection, and Isomap reflect only the dominant data structure, which may not align with the goals of downstream analysis or expert-provided annotations. Existing supervised variants only partially address this mismatch and introduce new limitations. Here we present RF-PHATE, a supervised visualization approach that incorporates expert knowledge to reveal label-relevant structure while suppressing extraneous variation. RF-PHATE uses random forests to learn relationships between features and labels and translates this information into low-dimensional embeddings. RF-PHATE handles large datasets and is suitable for both classification and regression tasks. We demonstrate its use across four case studies, including longitudinal multiple sclerosis data, Raman spectral measurements of antioxidant effects, outcomes of patients with COVID-19, and RNA sequencing data with simulated dropout. These applications highlight RF-PHATE’s ability to enhance interpretability, manage noise and expose meaningful biological structure, suggesting broad potential for improving data exploration and discovery. This study introduces RF-PHATE, a supervised visualization method that preserves data structure while revealing biological patterns, enabling insights into disease progression in multiple sclerosis, COVID-19 and other biological contexts.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 7","pages":"732-752"},"PeriodicalIF":20.3,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364694","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

Social technologies need societal alignment 社会技术需要社会一致性

IF 20.3

Nature computational science Pub Date : 2026-06-25 DOI: 10.1038/s43588-026-01004-x

Yara Kyrychenko, Sander van der Linden

引用次数: 0

The inequalities of GPU access GPU访问的不平等

IF 20.3

Nature computational science Pub Date : 2026-06-25 DOI: 10.1038/s43588-026-01013-w

引用次数: 0

Setting benchmarks for practical quantum utility of combinatorial optimization 为组合优化的实际量子效用设定基准

IF 20.3

Nature computational science Pub Date : 2026-06-23 DOI: 10.1038/s43588-026-00979-x

Pan Zhang

引用次数: 0

The Quantum Optimization Benchmarking Library 量子优化基准库

IF 20.3

Nature computational science Pub Date : 2026-06-23 DOI: 10.1038/s43588-026-00991-1

Thorsten Koch, David E. Bernal Neira, Ying Chen, Giorgio Cortiana, Daniel J. Egger, Raoul Heese, Narendra N. Hegade, Alejandro Gomez Cadavid, Rhea Huang, Toshinari Itoko, Thomas Kleinert, Pedro Maciel Xavier, Naeimeh Mohseni, Jhon A. Montanez-Barrera, Koji Nakano, Giacomo Nannicini, Corey O’Meara, Justin Pauckert, Manuel Proissl, Anurag Ramesh, Maximilian Schicker, Noriaki Shimada, Mitsuharu Takeori, Víctor Valls, David Van Bulck, Stefan Woerner, Christa Zoufal

{"title":"The Quantum Optimization Benchmarking Library","authors":"Thorsten Koch, David E. Bernal Neira, Ying Chen, Giorgio Cortiana, Daniel J. Egger, Raoul Heese, Narendra N. Hegade, Alejandro Gomez Cadavid, Rhea Huang, Toshinari Itoko, Thomas Kleinert, Pedro Maciel Xavier, Naeimeh Mohseni, Jhon A. Montanez-Barrera, Koji Nakano, Giacomo Nannicini, Corey O’Meara, Justin Pauckert, Manuel Proissl, Anurag Ramesh, Maximilian Schicker, Noriaki Shimada, Mitsuharu Takeori, Víctor Valls, David Van Bulck, Stefan Woerner, Christa Zoufal","doi":"10.1038/s43588-026-00991-1","DOIUrl":"10.1038/s43588-026-00991-1","url":null,"abstract":"Recent progress has brought benchmarking of (heuristic) quantum algorithms at scale within reach. Particularly in combinatorial optimization, it is key to empirically analyze and track progress towards quantum advantage. This work introduces a systematic, fair and comparable benchmarking framework for quantum optimization methods by presenting ten model-independent problem classes that are challenging for classical methods. Track records of specific instances and solutions are given in an accompanying open-source repository. While the individual properties of the problem classes vary, they all become challenging from less than 100 to, at most, an order of 100,000 decision variables. We reference results from state-of-the-art solvers for instances across all problem classes and demonstrate exemplary baseline results obtained with quantum solvers for selected problems, which illustrate standardized benchmark reporting. The presented problem instances may be approached with classical or quantum algorithms executed on varying hardware platforms to drive the field towards quantum advantage. This Resource presents the Quantum Optimization Benchmarking Library, which enables fair, reproducible benchmarks of quantum heuristics for ten difficult combinatorial optimization classes with baseline results to track progress towards quantum advantage.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 6","pages":"653-671"},"PeriodicalIF":20.3,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-026-00991-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314827","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

Evidence of scaling advantage on an NP-complete problem with enhanced quantum solvers 增强量子解算器在np完全问题上的尺度优势证据。

IF 20.3

Nature computational science Pub Date : 2026-06-19 DOI: 10.1038/s43588-026-01007-8

Quanfeng Lu, Shijie Wei, Keren Li, Pan Gao, Bao Yan, Muxi Zheng, Haoran Zhang, Jinfeng Zeng, Gui-Lu Long

{"title":"Evidence of scaling advantage on an NP-complete problem with enhanced quantum solvers","authors":"Quanfeng Lu, Shijie Wei, Keren Li, Pan Gao, Bao Yan, Muxi Zheng, Haoran Zhang, Jinfeng Zeng, Gui-Lu Long","doi":"10.1038/s43588-026-01007-8","DOIUrl":"10.1038/s43588-026-01007-8","url":null,"abstract":"Achieving quantum advantage remains a milestone in the noisy intermediate-scale quantum era. Without complexity proofs, scaling advantage—where quantum resource requirements grow more slowly than their classical counterparts—is the primary indicator. However, direct applications of quantum optimization algorithms to classically intractable problems have yet to demonstrate this advantage. Here we develop enhanced quantum solvers for the NP-complete one-in-three Boolean satisfiability problem. We propose a restricting space reduction algorithm that achieves optimal search-space dimensionality under mod-2 arithmetic, thereby reducing qubit requirements and time complexity. Numerical studies on instances with up to 70 variables demonstrate that our enhanced quantum approximate optimization algorithm- and quantum adiabatic algorithm-based solvers outperform state-of-the-art classical solvers; the quantum adiabatic algorithm-based solver serves as a lower-bound reference while retaining scaling advantage. Furthermore, experiments on a 13-qubit superconducting processor confirm the predicted improvements. Collectively, our results provide empirical evidence of quantum speedup for an NP-complete problem. This study develops enhanced quantum solvers for an NP-complete Boolean satisfiability problem and reports empirical scaling advantages over leading classical solvers in large-scale simulations, with supporting validation on a superconducting quantum processor.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 8","pages":"882-893"},"PeriodicalIF":20.3,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-026-01007-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148284974","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

Testing longitudinal gene–environment interactions 测试纵向基因-环境相互作用。

IF 20.3

Nature computational science Pub Date : 2026-06-16 DOI: 10.1038/s43588-026-01000-1

Han Chen

引用次数: 0

Leveraging longitudinal data to boost statistical power for gene–environment interaction analysis 利用纵向数据提高基因-环境相互作用分析的统计能力。

IF 20.3

Nature computational science Pub Date : 2026-06-16 DOI: 10.1038/s43588-026-01002-z

He Xu (, ), Yuzhuo Ma (, ), Yufei Liu (, ), Yin Li (, ), Lin Wan (, ), Ji-Feng Zhang (, ), Yanlong Zhao (, ), Weihua Yue (, ), Peipei Zhang (, ), Wenjian Bi (, )

{"title":"Leveraging longitudinal data to boost statistical power for gene–environment interaction analysis","authors":"He Xu \u0000 (, ), Yuzhuo Ma \u0000 (, ), Yufei Liu \u0000 (, ), Yin Li \u0000 (, ), Lin Wan \u0000 (, ), Ji-Feng Zhang \u0000 (, ), Yanlong Zhao \u0000 (, ), Weihua Yue \u0000 (, ), Peipei Zhang \u0000 (, ), Wenjian Bi \u0000 (, )","doi":"10.1038/s43588-026-01002-z","DOIUrl":"10.1038/s43588-026-01002-z","url":null,"abstract":"Gene–environment interaction (G×E) analyses play a crucial role in advancing genetic discovery, addressing missing heritability, and facilitating precision medicine. However, existing G×E methods are mostly designed for cross-sectional data, limiting the utility of longitudinal data. Here we propose SAGELD, a scalable and accurate genome-wide G×E method for longitudinal traits that controls for sample relatedness in large-scale datasets. SAGELD uses matrix projection to construct test statistics and the SPAGRM framework to efficiently control for sample relatedness, achieving 10- to 10,000-fold speedups over existing methods while maintaining greater power than cross-sectional analyses. We evaluated SAGELD through extensive simulations and UK Biobank analyses. Using age and body mass index as environmental exposures, we identified 74 loci with genetic × age interactions and 5 loci with genetic × adiposity interactions in the pooled analysis of longitudinal primary care data and cross-sectional assessment data. These results highlight the advantages of leveraging longitudinal data in G×E analyses. The authors introduce SAGELD, a method that leverages longitudinal data to detect gene–environment interactions with greater statistical power than conventional approaches based on cross-sectional data, improving discovery in large biobank studies.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 6","pages":"575-587"},"PeriodicalIF":20.3,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267095","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

Transferable human mobility network reconstruction with neuroGravity 基于神经重力的可转移人类移动网络重建。

IF 20.3

Nature computational science Pub Date : 2026-06-12 DOI: 10.1038/s43588-026-01003-y

Jinming Yang, Shaoyu Huang, Zongyuan Huang, Yaohui Jin, Xiaokang Yang, Marta C. González, Yanyan Xu

{"title":"Transferable human mobility network reconstruction with neuroGravity","authors":"Jinming Yang, Shaoyu Huang, Zongyuan Huang, Yaohui Jin, Xiaokang Yang, Marta C. González, Yanyan Xu","doi":"10.1038/s43588-026-01003-y","DOIUrl":"10.1038/s43588-026-01003-y","url":null,"abstract":"Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing mobility networks from publicly available data. Here we develop neuroGravity, a physics-informed deep learning model that reliably reconstructs mobility flows from limited observations and transfers to unobserved cities. Using only urban facility and population distributions, we find that neuroGravity’s regional representations strongly correlate with socioeconomic and livability status, offering scalable proxies for costly surveys. Furthermore, we uncover that spatial income segregation has a key role in model transferability: mobility networks are most reliably reconstructed when target cities share similar segregation levels with the source. We design an index to quantify this segregation and accurately predict transferability. Finally, we generate mobility flow proxies for over 1,200 cities worldwide, highlighting neuroGravity’s potential to mitigate critical data shortages in resource-limited, underdeveloped areas. This study presents neuroGravity—a model that merges physics with machine learning to reconstruct mobility networks in data-scarce regions. It provides crucial tools for equitable urban planning, revealing income segregation as a core determinant of human flows.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 6","pages":"630-641"},"PeriodicalIF":20.3,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148240642","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

Reconstructing urban mobility from the built environment 从建筑环境重构城市交通。

IF 20.3

Nature computational science Pub Date : 2026-06-12 DOI: 10.1038/s43588-026-01005-w

Qi R. Wang

引用次数: 0