Nature computational science最新文献_第2页

Generalized design of sequence-ensemble-function relationships for intrinsically disordered proteins. 内在无序蛋白质序列-集成-功能关系的广义设计。

IF 18.3

Nature computational science Pub Date : 2025-10-06 DOI: 10.1038/s43588-025-00881-y

Ryan K Krueger, Michael P Brenner, Krishna Shrinivas

引用次数: 0

Predicting drug responses of unseen cell types through transfer learning with foundation models. 基于基础模型的迁移学习预测未知细胞类型的药物反应。

IF 18.3

Nature computational science Pub Date : 2025-10-03 DOI: 10.1038/s43588-025-00887-6

Yixuan Wang, Xinyuan Liu, Yimin Fan, Binghui Xie, James Cheng, Kam Chung Wong, Peter Cheung, Irwin King, Yu Li

{"title":"Predicting drug responses of unseen cell types through transfer learning with foundation models.","authors":"Yixuan Wang, Xinyuan Liu, Yimin Fan, Binghui Xie, James Cheng, Kam Chung Wong, Peter Cheung, Irwin King, Yu Li","doi":"10.1038/s43588-025-00887-6","DOIUrl":"https://doi.org/10.1038/s43588-025-00887-6","url":null,"abstract":"<p><p>Drug repurposing through single-cell perturbation response prediction provides a cost-effective approach for drug development, but accurately predicting responses in unseen cell types that emerge during disease progression remains challenging. Existing methods struggle to achieve generalizable cell-type-specific predictions. To address these limitations, we introduce the cell-type-specific drug perturbatIon responses predictor (CRISP), a framework for predicting perturbation responses in previously unseen cell types at single-cell resolution. CRISP leverages foundation models and cell-type-specific learning strategies to enable effective transfer of information from control to perturbed states even with limited empirical data. Through systematic evaluation across increasingly challenging scenarios, from unseen cell types to cross-platform predictions, CRISP shows generalizability and performance improvements. We demonstrate CRISP's drug repurposing potential through zero-shot prediction from solid tumor data to sorafenib's therapeutic effects in chronic myeloid leukemia. The predicted anti-tumor mechanisms, including CXCR4 pathway inhibition, are supported by independent studies as an effective therapeutic strategy in chronic myeloid leukemia, aligning with past studies and clinical trials.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226337","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

Proteoform search from protein database with top-down mass spectra. 自顶向下质谱法在蛋白质数据库中搜索蛋白质形态。

IF 18.3

Nature computational science Pub Date : 2025-10-03 DOI: 10.1038/s43588-025-00880-z

Kunyi Li, Baozhen Shan, Lei Xin, Ming Li, Lusheng Wang

引用次数: 0

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

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 \u0000 (, ), Yihang Bao \u0000 (, ), An Gu \u0000 (, ), Weichen Song \u0000 (, ), Guan Ning Lin \u0000 (, )","doi":"10.1038/s43588-025-00878-7","DOIUrl":"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. EMO integrates DNA sequence and chromatin accessibility data to predict how noncoding variants regulate gene expression across tissues and single cells, enabling context-aware personalized insights into genetic effects for precision medicine.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"927-939"},"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

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":"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. This study presents a brain rhythm-based inference model (BRyBI) for speech processing in the auditory cortex. BRyBI shows how rhythmic neural activity enables robust speech processing by dynamically predicting context and elucidates mechanistic principles that allow robust speech parsing in the brain.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"915-926"},"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

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