Nature computational science最新文献_第5页

On writing accessible computational science papers 关于写通俗易懂的计算科学论文。

IF 18.3

Nature computational science Pub Date : 2025-07-24 DOI: 10.1038/s43588-025-00847-0

引用次数: 0

Guidelines for multi-fidelity Bayesian optimization of molecules and materials 分子和材料的多保真贝叶斯优化指南。

IF 18.3

Nature computational science Pub Date : 2025-07-23 DOI: 10.1038/s43588-025-00833-6

Qia Ke, Cory M. Simon

引用次数: 0

Best practices for multi-fidelity Bayesian optimization in materials and molecular research 材料和分子研究中多保真贝叶斯优化的最佳实践。

IF 18.3

Nature computational science Pub Date : 2025-07-23 DOI: 10.1038/s43588-025-00822-9

Víctor Sabanza-Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch

{"title":"Best practices for multi-fidelity Bayesian optimization in materials and molecular research","authors":"Víctor Sabanza-Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch","doi":"10.1038/s43588-025-00822-9","DOIUrl":"10.1038/s43588-025-00822-9","url":null,"abstract":"Multi-fidelity Bayesian optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. Here we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences. Multi-fidelity Bayesian optimization methods are studied on molecular and material discovery tasks, and guidelines are provided to recommend cheaper and informative low-fidelity sources when using this technique in experimental settings.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 7","pages":"572-581"},"PeriodicalIF":18.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700612","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

Learning committor-consistent collective variables 学习与提交者一致的集体变量。

IF 18.3

Nature computational science Pub Date : 2025-07-22 DOI: 10.1038/s43588-025-00834-5

Thorben Fröhlking, Simone Aureli, Francesco Luigi Gervasio

引用次数: 0

Advancing neural decoding with deep learning 用深度学习推进神经解码。

IF 18.3

Nature computational science Pub Date : 2025-07-11 DOI: 10.1038/s43588-025-00837-2

Ma Feilong, Yuqi Zhang

引用次数: 0

Privacy-preserving multicenter differential protein abundance analysis with FedProt 保护隐私的多中心差异蛋白丰度分析。

IF 18.3

Nature computational science Pub Date : 2025-07-11 DOI: 10.1038/s43588-025-00832-7

Yuliya Burankova, Miriam Abele, Mohammad Bakhtiari, Christine von Toerne, Teresa K. Barth, Lisa Schweizer, Pieter Giesbertz, Johannes R. Schmidt, Stefan Kalkhof, Janina Müller-Deile, Peter A. van Veelen, Yassene Mohammed, Elke Hammer, Lis Arend, Klaudia Adamowicz, Tanja Laske, Anne Hartebrodt, Tobias Frisch, Chen Meng, Julian Matschinske, Julian Späth, Richard Röttger, Veit Schwämmle, Stefanie M. Hauck, Stefan F. Lichtenthaler, Axel Imhof, Matthias Mann, Christina Ludwig, Bernhard Kuster, Jan Baumbach, Olga Zolotareva

{"title":"Privacy-preserving multicenter differential protein abundance analysis with FedProt","authors":"Yuliya Burankova, Miriam Abele, Mohammad Bakhtiari, Christine von Toerne, Teresa K. Barth, Lisa Schweizer, Pieter Giesbertz, Johannes R. Schmidt, Stefan Kalkhof, Janina Müller-Deile, Peter A. van Veelen, Yassene Mohammed, Elke Hammer, Lis Arend, Klaudia Adamowicz, Tanja Laske, Anne Hartebrodt, Tobias Frisch, Chen Meng, Julian Matschinske, Julian Späth, Richard Röttger, Veit Schwämmle, Stefanie M. Hauck, Stefan F. Lichtenthaler, Axel Imhof, Matthias Mann, Christina Ludwig, Bernhard Kuster, Jan Baumbach, Olga Zolotareva","doi":"10.1038/s43588-025-00832-7","DOIUrl":"10.1038/s43588-025-00832-7","url":null,"abstract":"Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises serious privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two: one at five centers from E. coli experiments and one at three centers from human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to the DEqMS method applied to pooled data, with completely negligible absolute differences no greater than 4 × 10−12. By contrast, −log10P computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25–26. In this Resource, the authors present FedProt, a tool that enables privacy-preserving, federated differential protein abundance analysis across multiple institutions. Its results match the results of centralized analysis, enabling secure, collaborative proteomics without sensitive data sharing.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 8","pages":"675-688"},"PeriodicalIF":18.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621448","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

Inter-individual and inter-site neural code conversion without shared stimuli 没有共享刺激的个体间和位点间神经编码转换。

IF 18.3

Nature computational science Pub Date : 2025-07-11 DOI: 10.1038/s43588-025-00826-5

Haibao Wang, Jun Kai Ho, Fan L. Cheng, Shuntaro C. Aoki, Yusuke Muraki, Misato Tanaka, Jong-Yun Park, Yukiyasu Kamitani

{"title":"Inter-individual and inter-site neural code conversion without shared stimuli","authors":"Haibao Wang, Jun Kai Ho, Fan L. Cheng, Shuntaro C. Aoki, Yusuke Muraki, Misato Tanaka, Jong-Yun Park, Yukiyasu Kamitani","doi":"10.1038/s43588-025-00826-5","DOIUrl":"10.1038/s43588-025-00826-5","url":null,"abstract":"Inter-individual variability in fine-grained functional topographies poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but they typically require paired brain data with the same stimuli between individuals, which are often unavailable. Here we present a neural code conversion method that overcomes this constraint by optimizing conversion parameters based on the discrepancy between the stimulus contents represented by original and converted brain activity patterns. This approach, combined with hierarchical features of deep neural networks as latent content representations, achieves conversion accuracies that are comparable with methods using shared stimuli. The converted brain activity from a source subject can be accurately decoded using the target’s pre-trained decoders, producing high-quality visual image reconstructions that rival within-individual decoding, even with data across different sites and limited training samples. Our approach offers a promising framework for scalable neural data analysis and modeling and a foundation for brain-to-brain communication. A neural code conversion method is introduced using deep neural network representations to align brain data across individuals without shared stimuli. The approach enables accurate inter-individual brain decoding and visual image reconstruction across sites.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 7","pages":"534-546"},"PeriodicalIF":18.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621447","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

A large-scale replication of scenario-based experiments in psychology and management using large language models 使用大型语言模型在心理学和管理学中大规模复制基于场景的实验。

IF 18.3

Nature computational science Pub Date : 2025-07-09 DOI: 10.1038/s43588-025-00840-7

Ziyan Cui, Ning Li, Huaikang Zhou

{"title":"A large-scale replication of scenario-based experiments in psychology and management using large language models","authors":"Ziyan Cui, Ning Li, Huaikang Zhou","doi":"10.1038/s43588-025-00840-7","DOIUrl":"10.1038/s43588-025-00840-7","url":null,"abstract":"We conducted a large-scale study replicating 156 psychological experiments from top social science journals using three state-of-the-art large language models (LLMs). Our results reveal that, while LLMs demonstrated high replication rates for main effects (73–81%) and moderate to strong success with interaction effects (46–63%), they consistently produced larger effect sizes than human studies. Notably, LLMs showed significantly lower replication rates for studies involving socially sensitive topics such as race, gender and ethics. When original studies reported null findings, LLMs produced significant results at remarkably high rates (68–83%); while this could reflect cleaner data with less noise, it also suggests potential risks of effect size overestimation. Our results demonstrate both the promises and the challenges of LLMs in psychological research: while LLMs are efficient tools for pilot testing and rapid hypothesis validation, enriching rather than replacing traditional human-participant studies, they require more nuanced interpretation and human validation for complex social phenomena and culturally sensitive research questions. Researchers replicated 156 psychological experiments using three large language models (LLMs) instead of human participants. LLMs achieved 73–81% replication rates but showed amplified effect sizes and challenges with socially sensitive topics.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 8","pages":"627-634"},"PeriodicalIF":18.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602414","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

Investigating the volume and diversity of data needed for generalizable antibody–antigen ΔΔG prediction 调查可推广抗体-抗原ΔΔG预测所需数据的数量和多样性。

IF 18.3

Nature computational science Pub Date : 2025-07-08 DOI: 10.1038/s43588-025-00823-8

Alissa M. Hummer, Constantin Schneider, Lewis Chinery, Charlotte M. Deane

{"title":"Investigating the volume and diversity of data needed for generalizable antibody–antigen ΔΔG prediction","authors":"Alissa M. Hummer, Constantin Schneider, Lewis Chinery, Charlotte M. Deane","doi":"10.1038/s43588-025-00823-8","DOIUrl":"10.1038/s43588-025-00823-8","url":null,"abstract":"Antibody–antigen binding affinity lies at the heart of therapeutic antibody development: efficacy is guided by specific binding and control of affinity. Here we present Graphinity, an equivariant graph neural network architecture built directly from antibody–antigen structures that achieves test Pearson’s correlations of up to 0.87 on experimental change in binding affinity (ΔΔG) prediction. However, our model, like previous methods, appears to be overtraining on the few hundred experimental data points available and performance is not robust to train–test cut-offs. To investigate the amount and type of data required to generalizably predict ΔΔG, we built synthetic datasets of nearly 1 million FoldX-generated and >20,000 Rosetta Flex ddG-generated ΔΔG values. Our results indicate that there are currently insufficient experimental data to accurately and robustly predict ΔΔG, with orders of magnitude more likely needed. Dataset size is not the only consideration; diversity is also an important factor for model predictiveness. These findings provide a lower bound on data requirements to inform future method development and data collection efforts. Predicting the effects of mutations on antibody–antigen binding is a key challenge in therapeutic antibody development. Orders of magnitude more data will be needed to unlock accurate, robust prediction.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 8","pages":"635-647"},"PeriodicalIF":18.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593098","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

Unbalanced gene-level batch effects in single-cell data 单细胞数据中不平衡基因水平的批效应。

IF 18.3

Nature computational science Pub Date : 2025-07-01 DOI: 10.1038/s43588-025-00829-2

引用次数: 0