Computational Core: Biostatistical Analysis and Network Modelling

计算核心：生物统计分析和网络建模

基本信息

批准号：
10622459
负责人：
David Gjertson
金额：
$ 21.47万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10622459
关键词：
Adopted Allogenic Animals Architecture Area Automobile Driving Biological Assay Biometry California Cells Clinical Clinical Research Complex Computer Analysis Computer Models Data Data Analyses Data Set Databases Development Dimensions Discriminant Analysis Disease Ensure Face Fire - disasters Future Generations Genes Goals Health Insurance Portability and Accountability Act Hepatic Histopathology Human Image Immune Immune response Immunological Models Immunophenotyping Injury Ischemia Los Angeles Machine Learning Manuscripts Meditation Methodology Methods Modeling Molecular Monitor Mus Myeloid Cells Noise Outcome Pathologic Pathway Analysis Pathway interactions Phase Preparation Procedures Process Proteins Reperfusion Injury Reporting Research Research Personnel Research Project Grants Resource Sharing Role Sampling Secure Services Signal Transduction Site System Training Transplantation Universities Visualization Water clinically relevant cloud based complex data cost data infrastructure data management data modeling data repository deep learning design digital experimental study generative adversarial network heterogenous data immunoregulation improved interest learning community liver ischemia liver transplantation longitudinal analysis machine learning method machine vision mouse model multimodal data network models neural network novel phenotypic data preclinical study predictive modeling programs repository response tool transcriptome transcriptome sequencing transcriptomics trend

项目摘要

PROJECT SUMMARY This application proposes three research Projects and three Cores for shared resources initiated and led by investigators at the University of California Los Angeles (UCLA), centered around the theme of investigating innate-adaptive immunoregulation in liver transplant ischemia/reperfusion Injury (IRI). Projects 1 and 2 focus on IRI meditation of immune responses following hepatic grafting in syngeneic and allogeneic murine models, respectively; whereas, Project 3 concentrates on elucidating the role of IRI on immunoregulation following human OLT. The Computational Core provides data management, biostatistical and computational expertise required for the proper analysis and network modelling of data generated by Projects. Modelling will allow the integration of findings from the three types of liver transplants. Services will be provided in the following areas:  Data monitoring and management.  A central infrastructure for data preprocessing of high throughput molecular data.  Biostatistical design, analysis and interpretation of project studies.  Statistical monitoring of studies to ensure interpretability of results.  Development of biostatistical methodology for statistical problems in these projects.  Modeling the immune-cell dynamics to interpret immune-phenotyping datasets.  Process digital histopathology images using a deep learning approach called generative adversarial networks (GAN) to aid in computational/digital quantification and prediction of expressed proteins and spatially resolved genes at pathologic features of interest.  Integrate molecular and cellular scale data and analyses using matrix factorization and tensor decomposition.  Assistance with manuscript preparation.

项目摘要该申请提出了三个研究项目和三个核心，用于发起和领导的共享资源加利福尼亚大学洛杉矶分校（UCLA）的调查人员以调查为主题肝移植缺血/再灌注损伤（IRI）中的先天自适应免疫调节（IRI）。项目1和2关注 IRI对合成和同种异体鼠模型的肝移植后的免疫调查的冥想，分别;鉴于，项目3集中于阐明IRI在免疫调节之后的作用人类OLT。计算核心提供数据管理，生物统计和计算专业知识对项目生成的数据进行适当分析和网络建模所必需的。建模将允许从三种类型的肝移植中整合发现。服务将在以下领域提供： 数据监视和管理。 用于数据预处理高通量分子数据的中央基础架构。 项目研究的生物统计设计，分析和解释。 对研究的统计监测以确保结果的解释性。 开发这些项目中统计问题的生物统计学方法。 对免疫细胞动力学进行建模以解释免疫型数据集。 使用深度学习方法来处理数字组织病理学图像，称为通用对手网络（GAN）有助于计算/数字量化和预测表达的蛋白质和具有感兴趣的病理特征的空间分辨基因。 使用矩阵分解和张量分解整合了分子和细胞尺度数据并分析。 协助手稿准备。