Nature computational science最新文献

{"title":"Author Correction: Spatial modeling algorithms for reactions and transport in biological cells","authors":"Emmet A. Francis, Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg, Christopher T. Lee, Marie E. Rognes, Padmini Rangamani","doi":"10.1038/s43588-025-00773-1","DOIUrl":"10.1038/s43588-025-00773-1","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"185-185"},"PeriodicalIF":12.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-025-00773-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124138","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}

{"title":"Biologically inspired graphs to explore massive genetic datasets","authors":"Ryan M. Layer","doi":"10.1038/s43588-024-00763-9","DOIUrl":"10.1038/s43588-024-00763-9","url":null,"abstract":"A recent study proposes a data structure that addresses crucial challenges related to storage and computation of large genome databases.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"97-98"},"PeriodicalIF":12.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076347","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}

{"title":"Multimodal learning for mapping genotype-phenotype dynamics.","authors":"Farhan Khodaee, Rohola Zandie, Elazer R Edelman","doi":"10.1038/s43588-024-00765-7","DOIUrl":"10.1038/s43588-024-00765-7","url":null,"abstract":"<p><p>How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, uncovered potential cross-tissue biomarkers and provided contextualized embeddings to investigate the polyfunctionality of genes shown for the von Willebrand factor (VWF) gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061684","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}

{"title":"Author Correction: Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning","authors":"Hao Tang,&nbsp;Brian Xiao,&nbsp;Wenhao He,&nbsp;Pero Subasic,&nbsp;Avetik R. Harutyunyan,&nbsp;Yao Wang,&nbsp;Fang Liu,&nbsp;Haowei Xu,&nbsp;Ju Li","doi":"10.1038/s43588-025-00767-z","DOIUrl":"10.1038/s43588-025-00767-z","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"184-184"},"PeriodicalIF":12.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-025-00767-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026218","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}

{"title":"Boosting AI with neuromorphic computing","authors":"","doi":"10.1038/s43588-025-00770-4","DOIUrl":"10.1038/s43588-025-00770-4","url":null,"abstract":"We highlight the important role of neuromorphic computing in enhancing the power efficiency and performance of AI.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"1-2"},"PeriodicalIF":12.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-025-00770-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017818","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}

{"title":"Memristors enabling probabilistic AI at the edge","authors":"Damien Querlioz","doi":"10.1038/s43588-024-00761-x","DOIUrl":"10.1038/s43588-024-00761-x","url":null,"abstract":"By combining several probabilistic AI algorithms, a recent study demonstrates experimentally that the inherent noise and variation in memristor nanodevices can be exploited as features for energy-efficient on-chip learning.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"7-8"},"PeriodicalIF":12.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017826","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}

{"title":"Efficient large language model with analog in-memory computing","authors":"Anand Subramoney","doi":"10.1038/s43588-024-00760-y","DOIUrl":"10.1038/s43588-024-00760-y","url":null,"abstract":"A recent study demonstrates through numerical simulations that implementing large language models based on sparse mixture-of-experts architectures on 3D in-memory computing technologies can substantially reduce energy consumption.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"5-6"},"PeriodicalIF":12.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017821","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}

{"title":"Energy-efficient multimodal zero-shot learning using in-memory reservoir computing","authors":"","doi":"10.1038/s43588-024-00762-w","DOIUrl":"10.1038/s43588-024-00762-w","url":null,"abstract":"To achieve an advanced neuromorphic computing system with brain-like energy efficiency and generalization capabilities, we propose a hardware–software co-design of in-memory reservoir computing. This co-design integrates a liquid state machine-based encoder with artificial neural network projections on a hybrid analog–digital system, demonstrating zero-shot learning for multimodal event data.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"11-12"},"PeriodicalIF":12.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980780","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}

{"title":"Bridging generations and cultures in mathematics and computer science","authors":"Alyssa April Dellow,&nbsp;Fatimah Abdul Razak","doi":"10.1038/s43588-024-00756-8","DOIUrl":"10.1038/s43588-024-00756-8","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"3-4"},"PeriodicalIF":12.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959947","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}

{"title":"Resistive memory-based zero-shot liquid state machine for multimodal event data learning","authors":"Ning Lin,&nbsp;Shaocong Wang,&nbsp;Yi Li,&nbsp;Bo Wang,&nbsp;Shuhui Shi,&nbsp;Yangu He,&nbsp;Woyu Zhang,&nbsp;Yifei Yu,&nbsp;Yue Zhang,&nbsp;Xinyuan Zhang,&nbsp;Kwunhang Wong,&nbsp;Songqi Wang,&nbsp;Xiaoming Chen,&nbsp;Hao Jiang,&nbsp;Xumeng Zhang,&nbsp;Peng Lin,&nbsp;Xiaoxin Xu,&nbsp;Xiaojuan Qi,&nbsp;Zhongrui Wang,&nbsp;Dashan Shang,&nbsp;Qi Liu,&nbsp;Ming Liu","doi":"10.1038/s43588-024-00751-z","DOIUrl":"10.1038/s43588-024-00751-z","url":null,"abstract":"The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore’s law and Von Neumann bottleneck, hinder the efficiency of digital computers. In addition, SNNs are characterized by their software training complexities. Here, to this end, we propose a hardware–software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain–machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83- and 393.07-fold reduction in training costs compared with state-of-the-art spiking recurrent neural network-based contrastive learning and prototypical networks, and a 23.34- and 160-fold improvement in energy efficiency compared with cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware. This study presents a neuromorphic computing platform capable of learning cross-modal, event-driven signals for efficient real-time knowledge generalization. It also achieves zero-shot transfer learning for multimodal data.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"37-47"},"PeriodicalIF":12.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959950","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}