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

生物图像信息学是计算生物学的新兴前沿，因为迫切需要超越人工检查图像到计算分析以加速科学发现。传统的方法通常采用浅层机器学习模型，其中计算手工制作的图像表示并用于模型构建。这些方法严重依赖于对数据和问题的先验知识来计算适当的图像表示。由于最近深度学习方法在图像相关领域的成功，这个项目的目标是开发先进的深度学习模型，用于从生物图像中自动表示学习。该项目还促进了新课程和实验室基础设施的发展，以吸引研究生、本科生和高中生，重点关注那些未被充分代表的群体。具体而言，本项目重点分析果蝇和小鼠的时空基因表达模式图像。关键的挑战在于如何捕捉生物问题的内在结构，以及如何在小的、手动标记的生物数据集上进行有效的模型训练。该项目开发了多实例、多任务、分层和正则化的深度学习模型，用于整合生物问题的结构。多实例和多任务模型分别捕获输入和输出之间的复杂关系。分层和正则化模型显式地对问题结构进行编码，并使结果可解释。此外，还开发了迁移和无监督学习方法，以便在小标记数据集上进行有效的模型训练。这是通过集成跨多个域的标记和未标记数据集来实现的。总之，这个项目有望产生一套先进的深度学习方法，用于高效和有效的生物图像分析。该奖项反映了美国国家科学基金会的法定使命，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。

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.