Automating Biomedical Data Analysis

该项目将创建自动化系统，用于分析大型多模态生物医学数据，以增强生物医学研究

This project will create automated systems for analyzing big multimodal biomedical data to enhance the educational and research infrastructure at Kent State University and beyond. The completed tasks will provide support and improve the ability to analyze data (accuracy and speed) for at least 50 research labs, as well as train well over 200 students via integration into research programs and the curriculum. We will 1) Create algorithms to automate processing of large spatial biomedical data to automatically extract and analyze thousands of cells, 2) Create methods to automate the processing of dynamic magnetic resonance imaging (MRI) data, 3) Empirically evaluate the new methods in an animal model of disease, and,4) Generate data relating to changes in cell populations in disease to provide new therapeutic avenues. As we accomplish these goals we will support and strengthen education in at least three areas; 1) Enhance student training in biomedical imaging research techniques in three labs, 2) Create recurring multi- disciplinary courses based around development of the resources including “Applied Biomedical Data Processing” and “Biological Image Analysis” , and, 3) Develop the infrastructure for continued use and development for end users with a cluster-based parallelized data processing system for students and researchers worldwide. Laser scanning and three-dimensional electron microscopy produce data consisting of thousands of sequential images making up large volumes of data. Functional and structural MRI systems are routinely used to scan subjects and patients over many months using multiple modalities (fMRI, diffusion weighted, T1/T2). These types of arrays can have thousands of images and/or discrete time-points per modality generating complex data requiring significant human time (days) to process where sub-sampling is frequently required. Our long term goal is to support all types of automated data analysis pertinent to human health, and as such a major focus of this project is to create an extensible platform and methods to fully support all computationally expensive data analysis. We will initially focus our efforts on creating tools for the automated extraction and analysis of glial cells from large microscopy data (massive spatial tissue maps), automate segmentation and analysis of dynamic MRI data and automate big data processing using parallel systems. The methods will be used to evaluate changes to cell populations occurring in an animal model of disease to identify new strategies and manipulations for treatments. This will significantly enhance the research and educational infrastructure at Kent State University and include the development of new methods to automate biomedical data analysis as well as resources for the automated application and continued use of these and existing routines by many research groups. Further, by the creation of new courses, and integration of the multidisciplinary research activities in diverse labs, hundreds of students will be trained in the application and development of the methods and techniques.