CAREER: Interpretable machine learning deciphers single-cell multi-modal data for understanding cell-type functional genomics in complex brains

职业：可解释的机器学习破译单细胞多模式数据，以了解复杂大脑中的细胞类型功能基因组学

• 批准号: 2144475
• 负责人: Daifeng Wang
• 金额: $ 61.32万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144475&HistoricalAwards=false
• 关键词: CAREER; Interpretable; machine; learning; deciphers

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Brains are made up of billions of cells with different functions and have dramatically drawn research and public attention. However, underlying molecular mechanisms of brain cell functions are unclear. Recent advances in single-cell technologies enable measuring different characteristics (multi-modal data) of thousands of individual cells in complex brains, such as gene expression patterns, cell shapes, and behaviors. However, integrating such complex multi-modal data and interpreting molecular mechanisms from the data for brain cell functions remains challenging. This project will develop machine learning methods to build roadmaps linking multi-modal data of brain cells, revealing unseen data connections, insights into biological mechanisms, and improving prediction of cellular phenotypes and functions. The developed methods will be open-source and available for broadening community use. The project will also foster the integration of research and education through STEM programs, seminars, courses, online learning and provide publicly available materials. These activities will enhance participation and scientific understanding of minorities, underrepresented groups, and families with intellectual or neurodevelopmental disabilities, especially for machine learning in brain research.The project will deliver novel machine learning methods to predict cellular phenotypes and functions from multi-modal data of single cells and decipher cell-type functional genomics and gene regulation, a key molecular mechanism in brain cell functions. Aim 1 will develop a manifold learning method to align general single-cell multi-modalities (beyond multi-omics) and identify genes for predicting other modalities of brain cells (e.g., electrophysiology and morphology). Aim 2 will develop a comparative network analysis to reveal the relationships of multiple cell-type gene regulatory networks, revealing potential novel cell-type conserved and specific regulatory mechanisms. Aim 3 will develop a deep neural network model to prioritize “multi-modal networks” linking potentially causal genes and networks and other modal features for cellular phenotypes and functions. The results of this project can be found at https://daifengwanglab.org/.This 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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。大脑由数十亿个具有不同功能的细胞组成，并引起了研究和公众的关注。然而，脑细胞功能的潜在分子机制尚不清楚。单细胞技术的最新进展使测量复杂大脑中数千个单个细胞的不同特征（多模态数据）成为可能，例如基因表达模式、细胞形状和行为。然而，整合这种复杂的多模态数据并从脑细胞功能的数据中解释分子机制仍然具有挑战性。该项目将开发机器学习方法，以建立连接脑细胞多模态数据的路线图，揭示看不见的数据连接，深入了解生物机制，并改善对细胞表型和功能的预测。所开发的方法将是开放源代码的，可供扩大社区使用。该项目还将通过STEM计划，研讨会，课程，在线学习和提供公开材料来促进研究和教育的整合。这些活动将提高少数民族、代表性不足的群体和智力或神经发育障碍家庭的参与和科学理解，特别是大脑研究中的机器学习，该项目将提供新的机器学习方法，从单细胞的多模态数据预测细胞表型和功能，并破译细胞类型功能基因组学和基因调控，这是脑细胞功能的关键分子机制。目标1将开发一种多方面的学习方法来对齐一般的单细胞多模态（超越多组学），并识别用于预测脑细胞其他模态的基因（例如，电生理学和形态学）。目的二是通过比较网络分析揭示多种细胞类型基因调控网络的相互关系，揭示潜在的新的细胞类型保守和特异的调控机制。目标3将开发一个深度神经网络模型，优先考虑“多模态网络”，将潜在的因果基因和网络以及细胞表型和功能的其他模态特征联系起来。该项目的结果可以在www.example.com上找到https://daifengwanglab.org/.This奖项反映了NSF的法定使命，并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。

项目成果

BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids.

• DOI: 10.1016/j.crmeth.2023.100409
• 发表时间: 2023-02-27
• 期刊: Cell reports methods

Joint variational autoencoders for multimodal imputation and embedding

• DOI: 10.1038/s42256-023-00663-z
• 发表时间: 2023-05-29
• 期刊: NATURE MACHINE INTELLIGENCE
• 影响因子: 23.8
• 作者: Kalafut，Noah Cohen;Huang，Xiang;Wang，Daifeng
• 通讯作者: Wang，Daifeng