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

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的多模态测量成为可能，这将导致对基本生物过程前所未有的洞察力和分辨率。该项目将利用先进的机器学习模型、高效的计算工具和用户友好的软件构建一个新的生物信息学框架，用于单细胞多组学数据分析。产出将在网上提供给公众，预计将影响生物研究界，并使从事单细胞数据工作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的数据集中提取知识。这项计划有助开发新的教育工具，以加强课程设计。少数民族学生和服务不足的人群将从事尖端的研究活动。该项目侧重于设计有原则的机器学习和生物信息学算法，用于分析大规模单细胞多组学数据，以创建促进生物学研究的工具包。特别是，研究小组将研究1)用于多模态单细胞数据集成的新型跨模态深度典型相关自监督自编码器；2)利用半监督深度神经网络研究单细胞RNA-seq数据与蛋白质标记物关联的新计算方法；3)利用结构语义信息和已识别的生物标记物来增强预测模型的解释算法。4)用于识别和推断单细胞数据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变压器的新型变分自编码器模型；6)用于单细胞数据分析的工具门户开发，以推进生物学研究；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。该项目在整合单细胞生物信息学的大规模机器学习和数据密集型计算方面具有创新性，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https://sites.pitt.edu/~heh45/NSF2225775.htmlThis上找到。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

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.