Nature computational science最新文献

Boosting power for time-to-event GWAS analysis affected by case ascertainment. 增强受案例确定影响的时间到事件GWAS分析的能力。

IF 18.3

Nature computational science Pub Date : 2025-10-02 DOI: 10.1038/s43588-025-00892-9

引用次数: 0

Self-driving labs for biotechnology. 生物技术的自动驾驶实验室。

IF 18.3

Nature computational science Pub Date : 2025-10-01 DOI: 10.1038/s43588-025-00885-8

Evan Collins, Robert Langer, Daniel G Anderson

引用次数: 0

Rhythm-based hierarchical predictive computations support acoustic-semantic transformation in speech processing. 基于节奏的分层预测计算支持语音处理中的声-语义转换。

IF 18.3

Nature computational science Pub Date : 2025-09-26 DOI: 10.1038/s43588-025-00876-9

Olesia Dogonasheva, Keith B Doelling, Denis Zakharov, Anne-Lise Giraud, Boris Gutkin

{"title":"Rhythm-based hierarchical predictive computations support acoustic-semantic transformation in speech processing.","authors":"Olesia Dogonasheva, Keith B Doelling, Denis Zakharov, Anne-Lise Giraud, Boris Gutkin","doi":"10.1038/s43588-025-00876-9","DOIUrl":"https://doi.org/10.1038/s43588-025-00876-9","url":null,"abstract":"Unraveling how humans understand speech despite distortions has long intrigued researchers. A prominent hypothesis highlights the role of multiple endogenous brain rhythms in forming the computational context to predict speech structure and content. Yet how neural processes may implement rhythm-based context formation remains unclear. Here we propose the brain rhythm-based inference model (BRyBI) as a possible neural implementation of speech processing in the auditory cortex based on the interaction of endogenous brain rhythms in a predictive coding framework. BRyBI encodes key rhythmic processes for parsing spectro-temporal representations of the speech signal into phoneme sequences and to govern the formation of the phrasal context. BRyBI matches patterns of human performance in speech recognition tasks and explains contradictory experimental observations of rhythms during speech listening and their dependence on the informational aspect of speech (uncertainty and surprise). This work highlights the computational role of multiscale brain rhythms in predictive speech processing.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180835","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

Predicting the regulatory impacts of noncoding variants on gene expression through epigenomic integration across tissues and single-cell landscapes. 通过跨组织和单细胞景观的表观基因组整合预测非编码变异对基因表达的调控影响。

IF 18.3

Nature computational science Pub Date : 2025-09-26 DOI: 10.1038/s43588-025-00878-7

Zhe Liu, Yihang Bao, An Gu, Weichen Song, Guan Ning Lin

{"title":"Predicting the regulatory impacts of noncoding variants on gene expression through epigenomic integration across tissues and single-cell landscapes.","authors":"Zhe Liu, Yihang Bao, An Gu, Weichen Song, Guan Ning Lin","doi":"10.1038/s43588-025-00878-7","DOIUrl":"https://doi.org/10.1038/s43588-025-00878-7","url":null,"abstract":"Noncoding mutations play a critical role in regulating gene expression, yet predicting their effects across diverse tissues and cell types remains a challenge. Here we present EMO, a transformer-based model that integrates DNA sequence with chromatin accessibility data (assay for transposase-accessible chromatin with sequencing) to predict the regulatory impact of noncoding single nucleotide polymorphisms on gene expression. A key component of EMO is its ability to incorporate personalized functional genomic profiles, enabling individual-level and disease-contextual predictions and addressing critical limitations of current approaches. EMO generalizes across tissues and cell types by modeling both short- and long-range regulatory interactions and capturing dynamic gene expression changes associated with disease progression. In benchmark evaluations, the pretraining-based EMO framework outperformed existing models, with fine-tuning small-sample tissues enhancing the model's ability to fit target tissues. In single-cell contexts, EMO accurately identified cell-type-specific regulatory patterns and successfully captured the effects of disease-associated single nucleotide polymorphisms in conditions, linking genetic variation to disease-relevant pathways.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180816","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

The rise of large language models 大型语言模型的兴起

IF 18.3

Nature computational science Pub Date : 2025-09-24 DOI: 10.1038/s43588-025-00890-x

引用次数: 0

Neuromorphic principles in self-attention hardware for efficient transformers 高效变压器自注意硬件的神经形态原理。

IF 18.3

Nature computational science Pub Date : 2025-09-16 DOI: 10.1038/s43588-025-00868-9

Nathan Leroux, Jan Finkbeiner, Emre Neftci

引用次数: 0

On the compatibility of generative AI and generative linguistics 论生成人工智能与生成语言学的兼容性。

IF 18.3

Nature computational science Pub Date : 2025-09-16 DOI: 10.1038/s43588-025-00861-2

Eva Portelance, Masoud Jasbi

引用次数: 0

Increasing alignment of large language models with language processing in the human brain. 大型语言模型与人类大脑中语言处理的日益一致。

IF 18.3

Nature computational science Pub Date : 2025-09-16 DOI: 10.1038/s43588-025-00863-0

Changjiang Gao, Zhengwu Ma, Jiajun Chen, Ping Li, Shujian Huang, Jixing Li

引用次数: 0

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