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.

单细胞组学技术的最新发展使基因组、转录组、表观基因组或蛋白质组规模的多模态测量成为可能，这将导致对基本生物过程的前所未有的洞察和解决。该项目将构建一个新的生物信息学框架，包括先进的机器学习模型、高效的计算工具和用户友好的软件，用于单细胞多组学数据分析。这些成果将在线向公众提供，预计将影响生物研究界，并使研究单细胞数据的科学家能够有效地测试生物学假设，特别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的教育工具，以加强课程设计。少数民族学生和得不到充分服务的人口将参与尖端研究活动。该项目的重点是设计原则性的机器学习和生物信息学算法，用于分析大规模单细胞多组学数据，以创建工具包，促进生物研究。特别是，研究团队将研究1）用于多模态单细胞数据集成的新的跨模态深度典型相关自监督自动编码器，2）通过半监督深度神经网络研究单细胞RNA-seq数据和蛋白质标记物关联的新计算方法，3）通过利用结构语义信息和识别的生物标记物增强预测模型的解释算法，4）用于从单细胞数据识别和推断条件依赖性的统计推断框架，5）用于超分辨率空间转录组学的基于新的Transformer的变分自编码器模型，6）用于单细胞数据分析以推进生物学研究的工具门户开发，以及7）使用真实的大规模单细胞数据验证所提出的方法和系统。该项目在整合大规模机器学习和数据密集型计算用于单细胞生物信息学方面具有创新性，将为生物机制理解和生物医学发展带来巨大希望。该项目的结果可在以下网址找到：https://sites.pitt.edu/~heh45/NSF2225775.htmlThis奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。