Collaborative Research: Integrated Moment-Based Descriptors and Deep Neural Network for Screening Three-Dimensional Biological Data

三维(3D)成像对于理解复杂的生物系统是必不可少的，因为它提供了关于器官、组织和分子的不可或缺的信息，而这些信息是无法使用二维捕获的。虽然现代成像方法产生了大量的图像数据，但仍有待开发出对这种体积数据集进行高效和有效分析的工具。该项目旨在为分析使用多种成像方式和各种数据类型获得的体积图像提供一般基础。这项研究旨在促进许多科学和技术领域的进步，在这些领域中，图像分析至关重要并具有重大的社会影响。它的主要应用将是生物分子识别和分类。预计这些方法还将适用于其他生物和医学3D数据检索以及其他学科中的其他类型3D数据，如人脸识别、地理和气候数据以及计算机辅助设计。该项目将利用普渡大学和圣约瑟夫大学跨学科计算生命科学和工程系的努力，通过多学科课程工作和直接参与该项目来招聘和培训学生。这两个机构将通过组织联合数学生物学会议和夏季本科生研究节来促进学生和教职员工参与这项研究。该项目旨在开发和整合两种互补和协同的方法：第一种是将数学矩扩展到包含分数阶矩描述符，从而提供更准确的3D图像表示。二是将新的基于矩的方法集成到深度神经网络中，以实现对3D数据的高精度和高效率的分类。最后，这些技术将被结合起来，实现一站式生物分子3D图像网络服务器，该服务器将公开使用，并用于筛选蛋白质配体结合口袋、功能位点和药物分子搜索。蛋白质结构将用体素网格表示，将值映射到3D网格点上。由于体素化在3D成像中非常普遍，新方法有望应用于其他成像学科的数据，如放射学(X射线、MRI、CT)和电子显微镜。将基于矩的方法和深度学习用于3D数据识别的新技术也有望对机器学习领域产生重大影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Three-dimensional (3D) imaging is essential for understanding complex biological systems, as it provides indispensable information about organs, tissues, and molecules that cannot be captured using two dimensions. While contemporary imaging methods produce an enormous amount of image data, tools for efficient and effective analysis of such volumetric data sets remain to be developed. This project aims to provide a general foundation for analyzing volumetric images obtained using multiple imaging modalities and for various data types. The research aims to contribute to progress in many science and technology domains in which image analysis is crucial and of significant societal impact. The primary application will be to biological molecular recognition and classification. The methods are also expected to apply to other biological and medical 3D data retrieval as well as other types of 3D data in other disciplines, such as human face recognition, geographical and climate data, and computer-aided design. The project will leverage efforts in the interdisciplinary computational life science and engineering departments at Purdue University and Saint Joseph’s University by recruiting and training students through multidisciplinary coursework and direct involvement with the project. The two institutions will foster student and faculty participation in this research by organizing a joint mathematical biology conference and a summer undergraduate research fest.This project aims to develop and integrate two complementary and synergistic methods: The first is to extend mathematical moments to encompass fractional-order moment descriptors and hence provide a more accurate representation of 3D images. The second is to integrate the new moment-based approach into a deep neural network to achieve high accuracy and efficiency in classifying 3D data. Finally, the techniques will be combined to implement a one-stop biomolecular 3D image web server, which will be publicly available and used for screening protein ligand-binding pockets, functional sites, and drug molecule search. Protein structures will be represented with voxel grids, mapping values onto 3D grid points. Because voxelization is highly prevalent in 3D imaging, the new methods are expected to apply to data from other imaging disciplines, such as radiology (x-ray, MRI, CT) and electron microscopy. The new techniques for integrating moment-based approaches and deep learning for 3D data recognition are also expected to substantially influence the machine learning domain.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.