III: Small: Deep Learning for Gene Expression Pattern Image Analysis

III：小：深度学习用于基因表达模式图像分析

• 批准号: 1811675
• 负责人: Shuiwang Ji
• 金额: $ 50万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811675&HistoricalAwards=false
• 关键词: III; Small; Deep; Learning; Gene

Biological image informatics is an emerging frontier in computational biology, as there is an urgent need to move beyond the manual inspection of images to computational analysis for accelerating scientific discoveries. Conventional methods commonly employ shallow machine learning models in which handcrafted image representations are computed and used in model construction. These approaches heavily rely on prior knowledge on the data and problems to compute appropriate image representations. Motivated by the recent success of deep learning methods in image-related domains, the objective of this project is to develop advanced deep learning models for automated representation learning from biological images. This project also facilitates the development of new courses and laboratory infrastructure for attracting graduate, undergraduate, and high school students, with an emphasis on those from underrepresented groups.Specifically, this project focuses on the analysis of spatiotemporal gene expression pattern images in fruit fly and mouse. The key challenges lie in how to capture the intrinsic structures of biological problems and how to enable effective model training on small, manually labeled biological data sets. This project develops multi-instance, multi-task, hierarchical, and regularized deep learning models for incorporating the structures of biological problems. The multi-instance and multi-task models capture the complex relationships among inputs and outputs, respectively. The hierarchical and regularized models explicitly encode problem structures and make the results interpretable. In addition, transfer and unsupervised learning methods are developed to enable effective model training on small labeled data sets. These are achieved by integrating both labeled and unlabeled data sets across multiple domains. Altogether, this project is expected to result in a set of advanced deep learning methods for the efficient and effective analysis of biological images.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.

生物图像信息学是计算生物学领域的新兴领域，因为迫切需要超越对图像的手动检查，以加快计算分析，以加速科学发现。常规方法通常采用浅机学习模型，其中计算手工制作的图像表示并在模型构造中使用。这些方法在很大程度上依赖于数据和问题的先验知识来计算适当的图像表示。由深度学习方法在与图像相关的领域中的最新成功所激发的动机，该项目的目的是开发从生物图像中学习的高级深度学习模型。该项目还促进了新课程和实验室基础设施的发展，以吸引毕业生，本科和高中生，并着重于代表性不足的群体。关键挑战在于如何捕获生物学问题的内在结构以及如何对小型，手动标记的生物数据集进行有效的模型培训。该项目开发了多个结构，多任务，分层和正规深度学习模型，用于融合生物学问题的结构。多任务和多任务模型分别捕获了输入和输出之间的复杂关系。分层和正规模型明确编码问题结构，并使结果可解释。此外，开发了转移和无监督的学习方法，以实现对小标签数据集的有效模型培训。这些是通过在多个域中集成标记和未标记的数据集来实现的。总的来说，该项目有望为生物图像的有效分析提供一系列先进的深度学习方法。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。