Nature computational science最新文献_第8页

Making the most of presubmission enquiries 充分利用提交前查询。

IF 18.3

Nature computational science Pub Date : 2025-05-22 DOI: 10.1038/s43588-025-00817-6

引用次数: 0

The critical role of persistent disruption in advancing science 持续的破坏在推动科学发展中的关键作用。

IF 18.3

Nature computational science Pub Date : 2025-05-20 DOI: 10.1038/s43588-025-00808-7

Nan Deng, Xifeng Gu, Ying Fan, Shlomo Havlin, An Zeng

{"title":"The critical role of persistent disruption in advancing science","authors":"Nan Deng, Xifeng Gu, Ying Fan, Shlomo Havlin, An Zeng","doi":"10.1038/s43588-025-00808-7","DOIUrl":"10.1038/s43588-025-00808-7","url":null,"abstract":"Disruptive innovation is an important feature of scientific research. However, increasing evidence in recent years shows that highly disruptive papers are not necessarily milestone works in science and may even receive very few citations. To understand the mechanisms leading to such phenomena, we develop a link disruption metric that quantifies the disruptiveness of each citation link. This metric allows us to investigate disruption at both the reference and citation levels, enabling the development of a two-dimensional framework to evaluate the persistence of disruption caused by a given paper. Surprisingly, we find that papers with high reference disruption can have high citation disruption, meaning that a paper that disrupts previous papers may itself be further disrupted by its later citing papers. We find that persistently disruptive papers (disruptive papers that are not disrupted by citing papers) are more likely to be recognized as award-winning papers and receive high numbers of citations. Finally, we find that papers of larger teams and papers in recent years, though found to have weaker disruption, are more likely to have stronger persistent disruption once they disrupt previous papers. This study introduces a concept of persistent disruption, which marks papers that disrupt previous work but remain undisrupted later, signaling lasting impact. Milestone papers are found to be consistently associated with persistent disruption.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 6","pages":"492-501"},"PeriodicalIF":18.3,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112881","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

Computational challenges arising in algorithmic fairness and health equity with generative AI 生成式人工智能在算法公平和健康公平方面产生的计算挑战。

IF 18.3

Nature computational science Pub Date : 2025-05-16 DOI: 10.1038/s43588-025-00806-9

Vinith M. Suriyakumar, Anna Zink, Maia Hightower, Marzyeh Ghassemi, Brett Beaulieu-Jones

引用次数: 0

Toward fair AI-driven medical text generation 朝着公平的人工智能驱动的医学文本生成。

IF 18.3

Nature computational science Pub Date : 2025-05-14 DOI: 10.1038/s43588-025-00807-8

Yumeng Zhang, Jiangning Song

引用次数: 0

DNA data storage for biomedical images using HELIX 利用HELIX存储生物医学图像的DNA数据。

IF 18.3

Nature computational science Pub Date : 2025-05-13 DOI: 10.1038/s43588-025-00793-x

Guanjin Qu, Zihui Yan, Xin Chen, Huaming Wu

{"title":"DNA data storage for biomedical images using HELIX","authors":"Guanjin Qu, Zihui Yan, Xin Chen, Huaming Wu","doi":"10.1038/s43588-025-00793-x","DOIUrl":"10.1038/s43588-025-00793-x","url":null,"abstract":"Deoxyribonucleic acid (DNA) data storage is expected to become a key medium for large-scale data. Biomedical data images typically require substantial storage space over extended periods, making them ideal candidates for DNA data storage. However, existing DNA data storage models are primarily designed for generic files and lack a comprehensive retrieval system for biomedical images. Here, to address this, we propose HELIX, a DNA-based storage system for biomedical images. HELIX introduces an image-compression algorithm tailored to the characteristics of biomedical images, achieving high compression rates and robust error tolerance. In addition, HELIX incorporates an error-correcting encoding algorithm that eliminates the need for indexing, enhancing storage density and decoding speed. We utilize a deep learning-based image repair algorithm for the predictive restoration of partially missing image blocks. In our in vitro experiments, we successfully stored two spatiotemporal genomics images. This sequencing process achieved 97.20% image quality at a depth of 7× coverage. A DNA-based storage system for biomedical images is proposed, combining compression, error correction and deep learning repair. It achieves 97.20% image quality at 7× coverage depth, demonstrating high compression and fault tolerance.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 5","pages":"397-404"},"PeriodicalIF":18.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043618","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

Leveraging generative models with periodicity-aware, invertible and invariant representations for crystalline materials design 利用具有周期性感知、可逆和不变表征的生成模型进行晶体材料设计。

IF 18.3

Nature computational science Pub Date : 2025-05-09 DOI: 10.1038/s43588-025-00797-7

Zhilong Wang, Fengqi You

引用次数: 0

Bi-level identification of governing equations for nonlinear physical systems 非线性物理系统控制方程的双水平辨识。

IF 18.3

Nature computational science Pub Date : 2025-05-09 DOI: 10.1038/s43588-025-00804-x

Zeyu Li, Huining Yuan, Wang Han, Yimin Hou, Hongjue Li, Haidong Ding, Zhiguo Jiang, Lijun Yang

{"title":"Bi-level identification of governing equations for nonlinear physical systems","authors":"Zeyu Li, Huining Yuan, Wang Han, Yimin Hou, Hongjue Li, Haidong Ding, Zhiguo Jiang, Lijun Yang","doi":"10.1038/s43588-025-00804-x","DOIUrl":"10.1038/s43588-025-00804-x","url":null,"abstract":"Identifying governing equations from observational data is crucial for understanding nonlinear physical systems but remains challenging due to the risk of overfitting. Here we introduce the Bi-Level Identification of Equations (BILLIE) framework, which simultaneously discovers and validates equations using a hierarchical optimization strategy. The policy gradient algorithm of reinforcement learning is leveraged to achieve the bi-level optimization. We demonstrate BILLIE’s superior performance through comparisons with baseline methods in canonical nonlinear systems such as turbulent flows and three-body systems. Furthermore, we apply the BILLIE framework to discover RNA and protein velocity equations directly from single-cell sequencing data. The equations identified by BILLIE outperform empirical models in predicting cellular differentiation states, underscoring BILLIE’s potential to reveal fundamental physical laws across a wide range of scientific fields. In this study the authors introduce BILLIE, a bi-level framework for equation identification that decouples term selection and quantification, enhancing robustness and accuracy in modeling nonlinear systems. BILLIE outperforms existing methods in handling complex systems and imperfect data, as demonstrated through both simulations and real-world biological applications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 6","pages":"456-466"},"PeriodicalIF":18.3,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052754","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

Everything everywhere all at once: a probability-based enhanced sampling approach to rare events 所有地方的一切都同时发生：一种基于概率的增强采样方法来处理罕见事件。

IF 18.3

Nature computational science Pub Date : 2025-05-05 DOI: 10.1038/s43588-025-00799-5

Enrico Trizio, Peilin Kang, Michele Parrinello

{"title":"Everything everywhere all at once: a probability-based enhanced sampling approach to rare events","authors":"Enrico Trizio, Peilin Kang, Michele Parrinello","doi":"10.1038/s43588-025-00799-5","DOIUrl":"10.1038/s43588-025-00799-5","url":null,"abstract":"The problem of studying rare events is central to many areas of computer simulations. We recently proposed an approach to solving this problem that involves computing the committor function, showing how it can be iteratively computed in a variational way while efficiently sampling the transition state ensemble. Here we greatly improve this procedure by combining it with a metadynamics-like enhanced sampling approach in which a logarithmic function of the committor is used as a collective variable. This procedure leads to an accurate sampling of the free energy surface in which transition states and metastable basins are studied with the same thoroughness. We show that our approach can be used in cases with the possibility of competing reactive paths and metastable intermediates. In addition, we demonstrate how physical insights can be obtained from the optimized committor model and the sampled data, thus providing a full characterization of the rare event under study. A single semi-automatic enhanced sampling method for rare events, based on machine-learned committor functions, allows simultaneous sampling of reactive events, calculation of free energy and understanding of transition states.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 7","pages":"582-591"},"PeriodicalIF":18.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045191","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 quest toward comprehensive benchmarking of quantum computing software 对量子计算软件的全面基准测试的探索。

IF 18.3

Nature computational science Pub Date : 2025-04-29 DOI: 10.1038/s43588-025-00803-y

Chenghong Zhu, Lei Zhang, Xin Wang

引用次数: 0

Physics-based modeling in the new era of enzyme engineering 酶工程新时代的物理建模。

IF 18.3

Nature computational science Pub Date : 2025-04-24 DOI: 10.1038/s43588-025-00788-8

Christopher Jurich, Qianzhen Shao, Xinchun Ran, Zhongyue J. Yang

引用次数: 0