Image-derived modeling

六. TR&D4 -摘要 该项目的总体目标是开发工具，以便最大限度地利用生物信息， 图像，使预测，多尺度模型的结构和动力学在亚细胞的建设 和细胞水平。这些工具对于研究细胞组织是如何形成的， 以及这种组织在不同的细胞类型和疾病期间是如何不同的。虽然现有 软件主要提供图像的描述，这个项目的重点是建设生成 细胞组织的模型。生成模型是从图像集合中学习的，并且能够 产生与用于训练的图像在统计上等效的新图像。这些模型 区别于区别性或描述性方法的明显优势。他们试图利用所有的信息 在图像中，而不仅仅是提取选定的描述符或特征。此外，虽然功能对于 不同实验室之间的比较和交流结果，因为它们依赖于 图像采集的细节，生成模型捕捉产生图像的潜在现实， 因此，可以在不同的显微镜和实验室进行比较。它们也是可组合的， 可重用，因为模型可以链接在一起，对新的关系进行预测， 针对一种细胞类型了解的细胞器形状和分布可以暂时扩展到新的细胞类型。 在之前的资助期间的工作导致了广泛的生成模型功能的开发， 这是开源的CellOrganizer系统。我们建议在这项工作的基础上建立新的 构建考虑细胞器之间广泛相互关系的模型的能力， 细胞中的结构，以及蛋白质和细胞器的动力学建模。与TR&D3一起，我们 还将开发新的方法，使用图像来约束估计的组成部分之间的亲和力， 一个生物系统最后，我们将开发新的方法来构建模型，从电子和 荧光显微镜图像。 拟议的工作利用了机器学习和计算机视觉中最好的方法，包括 高级推理方法和卷积神经网络（所谓的“深度学习”方法）。工作 建立在先前资助的TR&D3目标1下取得的广泛进展的基础上 在此期间，有11篇出版物承认了P41的支持。

VI. TR&D4 - Abstract The overall goal of this project is to develop tools for making maximal use of the information in biological images to enable the construction of predictive, multiscale models of structure and dynamics at the subcellular and cellular level. The tools will be especially useful for studies of how cell organization is created and maintained and how that organization differs from cell type to cell type and during disease. While existing software primarily provides descriptions of images, the focus of this project is on construction of generative models of cell organization. Generative models are learned from a collection of images and are capable of producing new images that are statistically equivalent to the images used for training. These models have distinct advantages over discriminative or descriptive approaches. They attempt to make use of all information in images, rather than to just extract selected descriptors or features. Further, while features are not useful for comparing and communicating results between different laboratories due to their dependence upon the specifics of image acquisition, generative models capture the underlying reality that gave rise to images and can therefore be compared across different microscopes and laboratories. They are also combinable and reusable, in that models can be linked together to make predictions about new relationships, and models for organelle shape and distribution learned for one cell type can be provisionally extended to new cell types. Work during the prior funding led to the development of extensive generative model capabilities that were incorporated into the open source CellOrganizer system. We propose to build upon this work to build new capabilities for constructing models that consider the extensive interrelationships between organelles and structures in cells, and for modeling the dynamics of proteins and organelles. In conjunction with TR&D3, we will also develop new methods for using images to constrain estimation of the affinities between components of a biological system. Lastly, we will develop new approaches for constructing models from both electron and fluorescence microscope images. The proposed work makes use of best available methods in machine learning and computer vision, including advanced inference methods and convolutional neural nets (so called “deep learning” methods). The work builds on the extensive progress that has been made under what was Aim 1 of TR&D3 in the prior funding period, which resulted in eleven publications that acknowledged P41 support.