Nature computational science最新文献_第10页

Accelerating scientific progress with preprints 利用预印本加快科学进步。

Nature computational science Pub Date : 2024-05-29 DOI: 10.1038/s43588-024-00641-4

引用次数: 0

Outsourcing eureka moments to artificial intelligence 将 "尤里卡时刻 "外包给人工智能

Nature computational science Pub Date : 2024-05-24 DOI: 10.1038/s43588-024-00633-4

Martijn Meeter

引用次数: 0

Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics 离散潜隐嵌入揭示单细胞表观组学中的细胞多样性

Nature computational science Pub Date : 2024-05-24 DOI: 10.1038/s43588-024-00634-3

Zhi Wei

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Automated discovery of symbolic laws governing skill acquisition from naturally occurring data 从自然发生的数据中自动发现支配技能习得的符号法则

Nature computational science Pub Date : 2024-05-24 DOI: 10.1038/s43588-024-00629-0

Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, Zongkai Yang

{"title":"Automated discovery of symbolic laws governing skill acquisition from naturally occurring data","authors":"Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, Zongkai Yang","doi":"10.1038/s43588-024-00629-0","DOIUrl":"10.1038/s43588-024-00629-0","url":null,"abstract":"Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algorithm was developed to tackle the issues of unobservable cognitive states and an algorithmic explosion in searching. A deep learning model is initially employed to determine the learner’s cognitive state and assess the feature importance. Symbolic regression algorithms are then used to parse the neural network model into algebraic equations. Experimental results show that the algorithm can accurately restore preset laws within a noise range in continuous feedback settings. When applied to Lumosity training data, the method outperforms traditional and recent models in fitness terms. The study reveals two new forms of skill acquisition laws and reaffirms some previous findings. This paper introduces an algorithm to uncover laws of skill acquisition from naturally occurring data. By combining deep learning and symbolic regression, it accurately identifies cognitive states and extracts algebraic equations.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 5","pages":"334-345"},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141147736","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

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies 通过数据驱动战略应对计算挑战，设计后摩尔时代的半导体材料和器件。

Nature computational science Pub Date : 2024-05-23 DOI: 10.1038/s43588-024-00632-5

Jiahao Xie, Yansong Zhou, Muhammad Faizan, Zewei Li, Tianshu Li, Yuhao Fu, Xinjiang Wang, Lijun Zhang

引用次数: 0

Shuffling haplotypes to share reference panels for imputation 对单倍型进行洗牌，以共享用于估算的参考面板。

Nature computational science Pub Date : 2024-05-22 DOI: 10.1038/s43588-024-00640-5

引用次数: 0

A resampling-based approach to share reference panels 基于重采样的共享参考面板方法。

Nature computational science Pub Date : 2024-05-14 DOI: 10.1038/s43588-024-00630-7

Théo Cavinato, Simone Rubinacci, Anna-Sapfo Malaspinas, Olivier Delaneau

{"title":"A resampling-based approach to share reference panels","authors":"Théo Cavinato, Simone Rubinacci, Anna-Sapfo Malaspinas, Olivier Delaneau","doi":"10.1038/s43588-024-00630-7","DOIUrl":"10.1038/s43588-024-00630-7","url":null,"abstract":"For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy. The authors develop the tool RESHAPE to share reference panels in a safer way. The genome–phenome links in reference panels can generate re-identification threats and RESHAPE breaks these links by shuffling haplotypes while preserving imputation accuracy.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 5","pages":"360-366"},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00630-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924051","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 multidimensional dataset for structure-based machine learning 基于结构的机器学习多维数据集。

Nature computational science Pub Date : 2024-05-14 DOI: 10.1038/s43588-024-00631-6

Matthew Holcomb, Stefano Forli

引用次数: 0

MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery MISATO：基于结构发现药物的蛋白质配体机器学习数据集。

Nature computational science Pub Date : 2024-05-10 DOI: 10.1038/s43588-024-00627-2

Till Siebenmorgen, Filipe Menezes, Sabrina Benassou, Erinc Merdivan, Kieran Didi, André Santos Dias Mourão, Radosław Kitel, Pietro Liò, Stefan Kesselheim, Marie Piraud, Fabian J. Theis, Michael Sattler, Grzegorz M. Popowicz

{"title":"MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery","authors":"Till Siebenmorgen, Filipe Menezes, Sabrina Benassou, Erinc Merdivan, Kieran Didi, André Santos Dias Mourão, Radosław Kitel, Pietro Liò, Stefan Kesselheim, Marie Piraud, Fabian J. Theis, Michael Sattler, Grzegorz M. Popowicz","doi":"10.1038/s43588-024-00627-2","DOIUrl":"10.1038/s43588-024-00627-2","url":null,"abstract":"Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule–ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein–ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein–ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models. MISATO is a database for structure-based drug discovery that combines quantum mechanics data with molecular dynamics simulations on ~20,000 protein–ligand structures. The artificial intelligence models included provide an easy entry point for the machine learning and drug discovery communities.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 5","pages":"367-378"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00627-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905262","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

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity 用于揭示细胞异质性的单细胞染色质可及性测序数据的离散潜在嵌入。

Nature computational science Pub Date : 2024-05-10 DOI: 10.1038/s43588-024-00625-4

Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang

{"title":"Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity","authors":"Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang","doi":"10.1038/s43588-024-00625-4","DOIUrl":"10.1038/s43588-024-00625-4","url":null,"abstract":"Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models—especially variational autoencoders—have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE’s capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively. A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 5","pages":"346-359"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905261","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