Nature computational science最新文献_第10页

Gaps in gender and socioeconomic mobility disparity studies 性别和社会经济流动性差异研究中的差距

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00667-8

Laetitia Gauvin

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Using labels to limit AI misuse in health 利用标签限制人工智能在卫生领域的滥用

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00676-7

Elaine O. Nsoesie, Marzyeh Ghassemi

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Harnessing the power of emerging computational capabilities for independent mobility for persons with disabilities 利用新兴计算能力为残疾人提供独立行动能力

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00692-7

Vinod Namboodiri

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The lost data: how AI systems censor LGBTQ+ content in the name of safety 丢失的数据：人工智能系统如何以安全之名审查 LGBTQ+ 内容

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00695-4

Sophia Chen

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Accelerating economic development in Latin America through overcoming access challenges to supercomputing infrastructure 通过克服超级计算基础设施的接入挑战加速拉丁美洲的经济发展

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00686-5

Joaquín Barroso-Flores

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Putting a spotlight on diversity, equity, and inclusion 聚焦多样性、公平性和包容性

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00702-8

引用次数: 0

Defining our future with generative AI 用生成式人工智能定义我们的未来

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00694-5

Siddharth Suri

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Joint inference of discrete and continuous factors captures variability across and within cell types 离散因子和连续因子的联合推断可捕捉细胞类型间和细胞类型内的变异性。

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Nature computational science Pub Date : 2024-09-23 DOI: 10.1038/s43588-024-00696-3

引用次数: 0

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS 利用 MMIDAS 联合推断单细胞数据集中的离散细胞类型和连续类型特异性变异性

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Nature computational science Pub Date : 2024-09-23 DOI: 10.1038/s43588-024-00683-8

Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül

{"title":"Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS","authors":"Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül","doi":"10.1038/s43588-024-00683-8","DOIUrl":"10.1038/s43588-024-00683-8","url":null,"abstract":"Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here we propose an unsupervised method, Mixture Model Inference with Discrete-coupled AutoencoderS (MMIDAS), which combines a generalized mixture model with a multi-armed deep neural network to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both unimodal and multimodal datasets. Clustering in high-dimensional spaces with a large number of clusters and identifying common aspects of within-cluster variability remain challenging. Here the authors develop an unsupervised method for this purpose and demonstrate it on brain single-cell datasets.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"706-722"},"PeriodicalIF":12.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317001","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

Biophysically interpretable inference of cell types from multimodal sequencing data 从多模态测序数据推断细胞类型的生物物理可解释性

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Nature computational science Pub Date : 2024-09-20 DOI: 10.1038/s43588-024-00689-2

Tara Chari, Gennady Gorin, Lior Pachter

{"title":"Biophysically interpretable inference of cell types from multimodal sequencing data","authors":"Tara Chari, Gennady Gorin, Lior Pachter","doi":"10.1038/s43588-024-00689-2","DOIUrl":"10.1038/s43588-024-00689-2","url":null,"abstract":"Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or ‘clusters’ in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for ‘clusters’ through the governing parameters of cellular processes. MeK-means clusters single-cell multimodal data by linking modalities through their biophysical relationships. We redefine clusters through transcription kinetics to reveal how RNA production and processing drive cellular diversity and disease.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"677-689"},"PeriodicalIF":12.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317000","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