Nature computational science最新文献_第2页

Applying weighted Cox regression to genome-wide association studies of time-to-event phenotypes. 将加权Cox回归应用于事件时间表型的全基因组关联研究。

IF 18.3

Nature computational science Pub Date : 2025-09-12 DOI: 10.1038/s43588-025-00864-z

Ying Li, Yuzhuo Ma, He Xu, Yaoyao Sun, Min Zhu, Weihua Yue, Wei Zhou, Wenjian Bi

{"title":"Applying weighted Cox regression to genome-wide association studies of time-to-event phenotypes.","authors":"Ying Li, Yuzhuo Ma, He Xu, Yaoyao Sun, Min Zhu, Weihua Yue, Wei Zhou, Wenjian Bi","doi":"10.1038/s43588-025-00864-z","DOIUrl":"10.1038/s43588-025-00864-z","url":null,"abstract":"With the growing availability of time-stamped electronic health records linked to genetic data in large biobanks and cohorts, time-to-event phenotypes are increasingly studied in genome-wide association studies. Although numerous Cox-regression-based methods have been proposed for a large-scale genome-wide association study, case ascertainment in time-to-event phenotypes has not been well addressed. Here we propose a computationally efficient Cox-based method, named WtCoxG, that accounts for case ascertainment by fitting a weighted Cox proportional hazards null model. A hybrid strategy incorporating saddlepoint approximation largely increases its accuracy when analyzing low-frequency and rare variants. Notably, by leveraging external minor allele frequencies from public resources, WtCoxG further boosts statistical power. Extensive simulation studies demonstrated that WtCoxG is more powerful than ADuLT and other Cox-based methods, while effectively controlling type I error rates. UK Biobank real data analysis validated that leveraging external minor allele frequencies contributes to the power gains of WtCoxG compared with ADuLT in the analysis of type 2 diabetes and coronary atherosclerosis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056460","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

Real-time raw signal genomic analysis using fully integrated memristor hardware. 实时原始信号基因组分析使用完全集成的忆阻器硬件。

IF 18.3

Nature computational science Pub Date : 2025-09-12 DOI: 10.1038/s43588-025-00867-w

Peiyi He, Shengbo Wang, Ruibin Mao, Mingrui Jiang, Sebastian Siegel, Giacomo Pedretti, Jim Ignowski, John Paul Strachan, Ruibang Luo, Can Li

{"title":"Real-time raw signal genomic analysis using fully integrated memristor hardware.","authors":"Peiyi He, Shengbo Wang, Ruibin Mao, Mingrui Jiang, Sebastian Siegel, Giacomo Pedretti, Jim Ignowski, John Paul Strachan, Ruibang Luo, Can Li","doi":"10.1038/s43588-025-00867-w","DOIUrl":"10.1038/s43588-025-00867-w","url":null,"abstract":"Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analysis. These technologies generate noisy analog signals that traditionally require basecalling and read mapping, both demanding costly data movement on von Neumann hardware. Here, to overcome this, we present a memristor-based hardware-software codesign that processes raw sequencer signals directly in analog memory, combining the two separated steps. By exploiting intrinsic device noise for locality-sensitive hashing and implementing parallel approximate searches in content-addressable memory, we experimentally showcase on-site applications, including infectious disease detection and metagenomic classification on a fully integrated memristor chip. Our experimentally validated analysis confirms the effectiveness of this approach on real-world tasks, achieving a 97.15% F1 score in virus raw signal mapping, with 51× speed-up and 477× energy saving over an application-specific integrated circuit. These results demonstrate that in-memory computing hardware provides a viable solution for integration with portable sequencers, enabling real-time and on-site genomic analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056487","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

A complete photonic integrated neuron for nonlinear all-optical computing. 用于非线性全光计算的完整光子集成神经元。

IF 18.3

Nature computational science Pub Date : 2025-09-12 DOI: 10.1038/s43588-025-00866-x

Tao Yan, Yanchen Guo, Tiankuang Zhou, Guocheng Shao, Shanglong Li, Ruqi Huang, Qionghai Dai, Lu Fang

{"title":"A complete photonic integrated neuron for nonlinear all-optical computing.","authors":"Tao Yan, Yanchen Guo, Tiankuang Zhou, Guocheng Shao, Shanglong Li, Ruqi Huang, Qionghai Dai, Lu Fang","doi":"10.1038/s43588-025-00866-x","DOIUrl":"https://doi.org/10.1038/s43588-025-00866-x","url":null,"abstract":"The field of photonic neural networks has experienced substantial growth, driven by its potential to enable ultrafast artificial intelligence inference and address the escalating demand for computing speed and energy efficiency. However, realizing nonlinearity-complete all-optical neurons is still challenging, constraining the performance of photonic neural networks. Here we report a complete photonic integrated neuron (PIN) with spatiotemporal feature learning capabilities and reconfigurable structures for nonlinear all-optical computing. By interleaving the spatiotemporal dimension of photons and leveraging the Kerr effect, PIN performs high-order temporal convolution and all-optical nonlinear activation monolithically on a silicon-nitride photonic chip, achieving neuron completeness of weighted interconnects and nonlinearities. We develop the PIN chip system and demonstrate its remarkable performance in high-accuracy image classification and human motion generation. PIN enables ultrafast spatialtemporal processing with a latency as low as 240 ps, paving the way for advancing machine intelligence into the subnanosecond regime.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056517","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

Confidential computing for population-scale genome-wide association studies with SECRET-GWAS SECRET-GWAS用于群体规模全基因组关联研究的保密计算。

IF 18.3

Nature computational science Pub Date : 2025-09-12 DOI: 10.1038/s43588-025-00856-z

Jonah Rosenblum, Juechu Dong, Satish Narayanasamy

{"title":"Confidential computing for population-scale genome-wide association studies with SECRET-GWAS","authors":"Jonah Rosenblum, Juechu Dong, Satish Narayanasamy","doi":"10.1038/s43588-025-00856-z","DOIUrl":"10.1038/s43588-025-00856-z","url":null,"abstract":"Genomic data from a single institution lacks global diversity representation, especially for rare variants and diseases. Confidential computing can enable collaborative genome-wide association studies (GWAS) without compromising privacy or accuracy. However, due to limited secure memory space and performance overheads, previous solutions fail to support widely used regression methods. Here we present SECRET-GWAS—a rapid, privacy-preserving, population-scale, collaborative GWAS tool. We discuss several system optimizations, including streaming, batching, data parallelization and reducing trusted hardware overheads to efficiently scale linear and logistic regression to over a thousand processor cores on an Intel SGX-based cloud platform. In addition, we protect SECRET-GWAS against several hardware side-channel attacks. SECRET-GWAS is an open-source tool and works with the widely used Hail genomic analysis framework. Our experiments on Azure’s Confidential Computing platform demonstrate that SECRET-GWAS enables multivariate linear and logistic regression GWAS queries on population-scale datasets from ten independent sources in just 4.5 and 29 minutes, respectively. Secure collaborative genome-wide association studies (GWAS) with population-scale datasets address gaps in genomic data. This work proposes SECRET-GWAS and system optimizations that overcome resource constraints and exploit parallelism, while maintaining privacy and accuracy.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 9","pages":"825-835"},"PeriodicalIF":18.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056513","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

Vision language models excel at perception but struggles with scientific reasoning. 视觉语言模型擅长感知，但难以进行科学推理。

IF 18.3

Nature computational science Pub Date : 2025-09-10 DOI: 10.1038/s43588-025-00871-0

引用次数: 0

SurFF: a foundation model for surface exposure and morphology across intermetallic crystals SurFF：金属间晶体表面暴露和形态的基础模型。

IF 18.3

Nature computational science Pub Date : 2025-09-09 DOI: 10.1038/s43588-025-00839-0

Jun Yin (, ), Honghao Chen (, ), Jiangjie Qiu (, ), Wentao Li (, ), Peng He (, ), Jiali Li (, ), Iftekhar A. Karimi, Xiaocheng Lan (, ), Tiefeng Wang (, ), Xiaonan Wang (, )

{"title":"SurFF: a foundation model for surface exposure and morphology across intermetallic crystals","authors":"Jun Yin \u0000 (, ), Honghao Chen \u0000 (, ), Jiangjie Qiu \u0000 (, ), Wentao Li \u0000 (, ), Peng He \u0000 (, ), Jiali Li \u0000 (, ), Iftekhar A. Karimi, Xiaocheng Lan \u0000 (, ), Tiefeng Wang \u0000 (, ), Xiaonan Wang \u0000 (, )","doi":"10.1038/s43588-025-00839-0","DOIUrl":"10.1038/s43588-025-00839-0","url":null,"abstract":"With approximately 90% of industrial reactions occurring on surfaces, the role of heterogeneous catalysts is paramount. Currently, accurate surface exposure prediction is vital for heterogeneous catalyst design, but it is hindered by the high costs of experimental and computational methods. Here we introduce a foundation force-field-based model for predicting surface exposure and synthesizability (SurFF) across intermetallic crystals, which are essential materials for heterogeneous catalysts. We created a comprehensive intermetallic surface database using an active learning method and high-throughput density functional theory calculations, encompassing 12,553 unique surfaces and 344,200 single points. SurFF achieves density-functional-theory-level precision with a prediction error of 3 meV Å−2 and enables large-scale surface exposure prediction with a 105-fold acceleration. Validation against computational and experimental data both show strong alignment. We applied SurFF for large-scale predictions of surface energy and Wulff shapes for over 6,000 intermetallic crystals, providing valuable data for the community. A foundation machine learning model, SurFF, enables DFT-accurate predictions of surface energies and morphologies in intermetallic catalysts, achieving over 105-fold acceleration for high-throughput materials screening.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 9","pages":"782-792"},"PeriodicalIF":18.3,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031334","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

Using LLMs to advance the cognitive science of collectives 利用法学硕士推进集体认知科学。

IF 18.3

Nature computational science Pub Date : 2025-09-09 DOI: 10.1038/s43588-025-00848-z

Ilia Sucholutsky, Katherine M. Collins, Nori Jacoby, Bill D. Thompson, Robert D. Hawkins

引用次数: 0

Urban planning in the era of large language models 大语言时代的城市规划模型。

IF 18.3

Nature computational science Pub Date : 2025-09-08 DOI: 10.1038/s43588-025-00846-1

Yu Zheng, Fengli Xu, Yuming Lin, Paolo Santi, Carlo Ratti, Qi R. Wang, Yong Li

引用次数: 0

The other AI revolution: how the Global South is building and repurposing language models that speak to billions 另一场人工智能革命：全球南方如何建立和重新利用与数十亿人对话的语言模型。

IF 18.3

Nature computational science Pub Date : 2025-09-08 DOI: 10.1038/s43588-025-00865-y

Pedro Burgos

引用次数: 0

Overcoming computational bottlenecks in large language models through analog in-memory computing 通过模拟内存计算克服大型语言模型中的计算瓶颈。

IF 18.3

Nature computational science Pub Date : 2025-09-08 DOI: 10.1038/s43588-025-00860-3

Yudeng Lin, Jianshi Tang

引用次数: 0