Nature computational science最新文献_第2页

Universal restoration of medical images 医学图像的普遍恢复。

IF 18.3

Nature computational science Pub Date : 2026-04-03 DOI: 10.1038/s43588-026-00975-1

Yide Zhang

引用次数: 0

Benchmarking alignment methods for spatial transcriptomics data. 空间转录组学数据的基准比对方法。

IF 18.3

Nature computational science Pub Date : 2026-04-03 DOI: 10.1038/s43588-026-00977-z

Yunzhi Yan, Tianyi Gu, Chengcheng Sun, Yinghao Zhang, Yan Cui, Senlin Lin, Qi Zou, Yixuan Du, Chuangyi Han, Kairan Kang, Sijie Li, Yihan Zhao, Zhihong Lin, Zhiyuan Yuan, Bin-Zhi Qian

{"title":"Benchmarking alignment methods for spatial transcriptomics data.","authors":"Yunzhi Yan, Tianyi Gu, Chengcheng Sun, Yinghao Zhang, Yan Cui, Senlin Lin, Qi Zou, Yixuan Du, Chuangyi Han, Kairan Kang, Sijie Li, Yihan Zhao, Zhihong Lin, Zhiyuan Yuan, Bin-Zhi Qian","doi":"10.1038/s43588-026-00977-z","DOIUrl":"https://doi.org/10.1038/s43588-026-00977-z","url":null,"abstract":"Reconstructing the three-dimensional molecular architecture of tissues from two-dimensional spatial transcriptomics slices is a central goal in spatial biology. Spatial alignment, the computational registration of multiple tissue slices using their spatial coordinates and gene expression profiles, provides the foundational framework for this integrative perspective. Although numerous alignment methods have emerged, a comprehensive benchmark to guide their application has been notably absent. Here we address this by systematically evaluating a diverse suite of leading methods. Executing 295 distinct alignment tasks across diverse datasets and technologies, our framework quantifies method accuracy, efficiency, usability and robustness, while also assessing the downstream impact of alignment quality. Crucially, our study systematically investigates performance in challenging real-world scenarios, uncovering substantial limitations in current tools. To address these bottlenecks, we propose and validate effective mitigation strategies. Finally, we provide practical guidelines to assist researchers in selecting the optimal alignment method and optimizing their analytical workflows.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617211","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

To appeal or not to appeal 上诉或不上诉。

IF 18.3

Nature computational science Pub Date : 2026-03-27 DOI: 10.1038/s43588-026-00980-4

引用次数: 0

Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology. 大规模基础模型的规模化和量化使网络生物学中的资源效率预测成为可能。

IF 18.3

Nature computational science Pub Date : 2026-03-27 DOI: 10.1038/s43588-026-00972-4

Han Chen, Madhavan S Venkatesh, Javier Gόmez Ortega, Siddharth V Mahesh, Tarak N Nandi, Ravi K Madduri, Karin Pelka, Christina V Theodoris

{"title":"Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology.","authors":"Han Chen, Madhavan S Venkatesh, Javier Gόmez Ortega, Siddharth V Mahesh, Tarak N Nandi, Ravi K Madduri, Karin Pelka, Christina V Theodoris","doi":"10.1038/s43588-026-00972-4","DOIUrl":"10.1038/s43588-026-00972-4","url":null,"abstract":"Foundation models for network biology are pretrained on large-scale biological data to enable context-aware predictions in a diverse array of downstream tasks through transfer learning. However, increasing model sizes with the expansion of available pretraining data also increases the computational resources required for fine-tuning and inference in downstream applications. Here we first assemble a corpus comprising ~104 million human single-cell transcriptomes from a broad range of tissues and diseases and pretrain successively larger models, defining the scaling laws for transcriptional masked learning. We then demonstrate that model quantization preserves the contextual gene and cell embedding space of the full-precision model, matching performance in zero-shot and fine-tuning applications while requiring only 15% of the time and 34% of the memory as the full model for fine-tuning with the same batch size. Overall, model quantization represents an effective method for resource-efficient fine-tuning and inference while preserving biological knowledge.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535126","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

Progress and prospects of density functional development 密度功能开发的进展与展望。

IF 18.3

Nature computational science Pub Date : 2026-03-27 DOI: 10.1038/s43588-026-00969-z

Donald G. Truhlar, Dayou Zhang, Yinan Shu

引用次数: 0

HorusEye: a self-supervised foundation model for generalizable X-ray tomography restoration HorusEye：一种自我监督的x射线断层扫描复原基础模型。

IF 18.3

Nature computational science Pub Date : 2026-03-27 DOI: 10.1038/s43588-026-00973-3

Yuetan Chu, Longxi Zhou, Gongning Luo, Kai Kang, Suyu Dong, Zhongyi Han, Lianming Wu, Xianglin Meng, Changchun Yang, Xin Guo, Yuan Cheng, Yuan Qi, Xin Liu, Dexuan Xie, Yue Li, Ricardo Henao, Xigang Xiao, Shaodong Cao, Gianluca Setti, Zhaowen Qiu, Xin Gao

{"title":"HorusEye: a self-supervised foundation model for generalizable X-ray tomography restoration","authors":"Yuetan Chu, Longxi Zhou, Gongning Luo, Kai Kang, Suyu Dong, Zhongyi Han, Lianming Wu, Xianglin Meng, Changchun Yang, Xin Guo, Yuan Cheng, Yuan Qi, Xin Liu, Dexuan Xie, Yue Li, Ricardo Henao, Xigang Xiao, Shaodong Cao, Gianluca Setti, Zhaowen Qiu, Xin Gao","doi":"10.1038/s43588-026-00973-3","DOIUrl":"10.1038/s43588-026-00973-3","url":null,"abstract":"X-ray tomography is widely used across scientific and clinical domains, yet image degradation remains a major obstacle to reliable analysis, particularly under low-dose or data-scarce conditions. Existing restoration methods are typically designed for specific modalities and predefined degradation, limiting their generalizability. Here we show that image restoration can instead be formulated as learning realistic, nonparametric acquisition degradation processes directly from data. We introduce HorusEye, a self-supervised foundation model for X-ray tomography restoration that leverages interslice contrastive pretraining to jointly learn structural priors and degradation without paired supervision or predefined assumptions. Trained on over 100 million images, HorusEye generalizes across diverse modalities, restoration tasks and previously unseen imaging modalities, consistently outperforming task-specific approaches. Extensive evaluations demonstrate improved photon efficiency and recovery of high-frequency information. Clinical studies further demonstrate enhanced detectability of low-contrast anatomy and lesions, as well as improved performance on downstream tasks, highlighting HorusEye as a general postprocessing tool for X-ray tomography. HorusEye is a foundation model for universal X-ray tomography restoration that learns realistic degradation directly from data. It supports imaging at substantially lower doses and reduces hardware requirements while improving expert analysis and downstream AI performance.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 4","pages":"372-387"},"PeriodicalIF":18.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535097","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

De novo design of functional nucleic acids of aptamers 适配体功能核酸的从头设计。

IF 18.3

Nature computational science Pub Date : 2026-03-11 DOI: 10.1038/s43588-026-00965-3

Zhiming Zhang, Meng Jiang, Axin He, Youyuan Zhu, Ercheng Wang, Liqi Wan, Jiezhong Qiu, Pei Guo, Guangyong Chen, Da Han

{"title":"De novo design of functional nucleic acids of aptamers","authors":"Zhiming Zhang, Meng Jiang, Axin He, Youyuan Zhu, Ercheng Wang, Liqi Wan, Jiezhong Qiu, Pei Guo, Guangyong Chen, Da Han","doi":"10.1038/s43588-026-00965-3","DOIUrl":"10.1038/s43588-026-00965-3","url":null,"abstract":"Functional nucleic acids (FNAs) are essential elements for designing advanced molecular tools, yet their de novo design faces challenges due to the vast sequence space and inefficiency of experimental screening methods. Nucleic acid large language models (NA-LLMs) offer new opportunities for FNA design, but their generative capability remains underexplored. Here we introduce InstructNA, a framework leveraging NA-LLMs and high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX) to guide de novo design of FNAs without relying on structural information. InstructNA encodes semantically rich FNA representations and robustly decodes FNA sequences, enabling the generation of various types of FNA such as transcription factor-binding DNA and protein-binding aptamers with enhanced functionality and high sequence diversity. Compared with the traditional HT-SELEX, InstructNA generates 100% and 200% more strong aptamer binders for two protein targets, with a sequence similarity to the original HT-SELEX aptamers as low as 38%. These results underscore the efficacy and robustness of InstructNA, demonstrating its potential for FNA design. InstructNA leverages nucleic acid large language models with HT-SELEX for de novo generation of functional nucleic acids, exhibiting high efficiency and general applicability in designing aptamers for various targets.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 4","pages":"341-349"},"PeriodicalIF":18.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-026-00965-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147437866","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

BrainParc: unified lifespan brain parcellation from structural magnetic resonance images. BrainParc：从结构磁共振图像中统一的寿命脑分割。

IF 18.3

Nature computational science Pub Date : 2026-03-11 DOI: 10.1038/s43588-026-00963-5

Jiameng Liu, Feihong Liu, Kaicong Sun, Zhiming Cui, Tianyang Sun, Zehong Cao, Jiawei Huang, Shuwei Bai, Yulin Wang, Yulong Dou, Kaicheng Zhang, Caiwen Jiang, Yuyan Ge, Han Zhang, Feng Shi, Dinggang Shen

{"title":"BrainParc: unified lifespan brain parcellation from structural magnetic resonance images.","authors":"Jiameng Liu, Feihong Liu, Kaicong Sun, Zhiming Cui, Tianyang Sun, Zehong Cao, Jiawei Huang, Shuwei Bai, Yulin Wang, Yulong Dou, Kaicheng Zhang, Caiwen Jiang, Yuyan Ge, Han Zhang, Feng Shi, Dinggang Shen","doi":"10.1038/s43588-026-00963-5","DOIUrl":"https://doi.org/10.1038/s43588-026-00963-5","url":null,"abstract":"Accurate brain parcellation from structural MRI across the human lifespan is essential for advancing neuroimaging and neuroscience studies. However, existing methods often struggle to generalize owing to intensity and contrast variations across brain maturation, aging and differences in MRI acquisition protocols, limiting their clinical and research utility. Here we present BrainParc, a unified parcellation framework that leverages anatomical information invariant to intensity and contrast, enabling accurate, robust and longitudinally consistent parcellation across a heterogeneous dataset without the need for fine-tuning. Extensive experiments on both internal and external datasets demonstrate that BrainParc substantially outperforms state-of-the-art methods in delineating 106 brain regions. BrainParc consistently shows better performance across diverse populations and imaging conditions, both quantitatively and qualitatively. Beyond anatomical segmentation, we show that BrainParc enables reliable tracking of brain development and facilitates early diagnosis of neurological disorders, underscoring its potential as a robust and generalizable tool for large-scale neuroimaging studies and clinical translation.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147437820","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

Choreographing molecular design with TANGO 用TANGO编排分子设计。

IF 18.3

Nature computational science Pub Date : 2026-03-10 DOI: 10.1038/s43588-026-00966-2

Tiago Rodrigues

引用次数: 0

High-throughput transition-state searches in zeolite nanopores. 沸石纳米孔的高通量过渡态搜索。

IF 18.3

Nature computational science Pub Date : 2026-03-09 DOI: 10.1038/s43588-026-00964-4

Pau Ferri-Vicedo, Alexander J Hoffman, Avni Singhal, Rafael Gómez-Bombarelli

引用次数: 0