A New Machine Learning Framework for Single-Cell Multi-Omics Bioinformatics

单细胞多组学生物信息学的新机器学习框架

• 批准号: 2405416
• 负责人: Heng Huang
• 金额: $ 78.87万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2405416&HistoricalAwards=false
• 关键词: New; Machine; Learning; Framework; Single

Recent developments of single-cell omics technologies enable multi-modality measurements at genome, transcriptome, epigenome, or proteome scale, which will lead to unprecedented insight and resolution to fundamental biological processes. The project will construct a novel bioinformatics framework with advanced machine learning models, efficient computational tools, and user-friendly software for single-cell multi-omics data analysis. The outputs will be available online to the public and are expected to impact biological research community and empower scientists working on single-cell data to effectively test biological hypothesis, especially knowledge extraction from massive high-dimensional and complex datasets. The project will facilitate the development of novel educational tools to enhance curriculum design. Minority students and under-served populations will be engaged in cutting-edge research activities. The project focuses on designing principled machine learning and bioinformatics algorithms for analyzing large-scale single-cell multi-omics data to create toolkits to facilitate biological research. Specially, the research team will investigate 1) new cross-modal deep canonical correlation self-supervised autoencoder for multi-modal single-cell data integration, 2) new computational methods to study the associations of single-cell RNA-seq data and protein markers via semi-supervised deep neural networks, 3) interpretation algorithms to enhance predictive model via utilizing structure semantic information and identified biomarkers, 4) statistical inference framework for identifying and inferring conditional dependence from single-cell data, 5) novel transformer based variational autoencoder model for super-resolution spatial transcriptomics, 6) tool portal development for single-cell data analysis to advance biology research, and 7) validations of the proposed methods and system using real large-scale single-cell data. The project is innovative in integrating large-scale machine learning and data-intensive computing for single-cell bioinformatics and will hold great promise for biological mechanism understanding and biomedicine development. The results of the project can be found at: https://sites.pitt.edu/~heh45/NSF2225775.htmlThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

单细胞OMICS技术的最新发展可以在基因组，转录组，表观基因组或蛋白质组量表上进行多模式测量，这将导致前所未有的见识和解决基本生物学过程。该项目将使用高级机器学习模型，有效的计算工具和用户友好的软件来构建一个新颖的生物信息学框架，用于单细胞多摩变数据分析。这些产出将在线向公众提供，并有望影响生物学研究界，并赋予从事单细胞数据的科学家的能力，以有效地测试生物学假设，尤其是从大量高维和复杂数据集中提取知识。该项目将促进开发新颖的教育工具，以增强课程设计。少数民族学生和服务不足的人群将参与尖端的研究活动。该项目着重于设计有原则的机器学习和生物信息学算法，用于分析大型单细胞多摩s数据，以创建工具包以促进生物学研究。特别是，研究团队将研究1）多模式的单细胞数据集成的新型跨模式深型自动审查自动审查自动编码器，2）研究单细胞RNA-SES数据数据和蛋白质标记物的关联的新计算方法，通过半渗透的深度神经网络，3）识别量化量化藻类的识别量，3）生物标志物，4）用于识别和推断单细胞数据中的条件依赖性的统计推理框架，5）用于超分辨率空间转录组学的基于新颖的变压器的新型变形器自动编码器模型，6）单细胞数据分析的工具门户网站以推进生物学研究，7）使用真实的大型单核数据进行验证。该项目在整合单细胞生物信息学的大型机器学习和数据密集型计算方面具有创新性，并将对生物学机制理解和生物医学开发持巨大希望。该项目的结果可以在以下网址找到：https：//sites.pitt.edu/~heh45/nsf222225775.htmlthis Award反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来获得支持的。