Computational Core: Biostatistical Analysis and Network Modelling

计算核心：生物统计分析和网络建模

• 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
• 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

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.

项目概要 本申请提出了三个研究项目和三个共享资源核心，由以下机构发起和领导： 加州大学洛杉矶分校 (UCLA) 的研究人员围绕调查主题 肝移植缺血/再灌注损伤（IRI）中的先天适应性免疫调节。项目 1 和 2 重点关注 IRI 对同基因和同种异体小鼠模型肝移植后免疫反应的冥想， 分别;而项目 3 的重点是阐明 IRI 对免疫调节的作用，如下： 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正确分析和网络建模所需的。建模将允许 整合三种类型肝移植的研究结果。将在以下领域提供服务：  数据监控和管理。  用于高通量分子数据数据预处理的中央基础设施。  项目研究的生物统计设计、分析和解释。  对研究进行统计监测，以确保结果的可解释性。  针对这些项目中的统计问题开发生物统计方法。  对免疫细胞动力学进行建模以解释免疫表型数据集。 使用称为生成对抗的深度学习方法处理数字组织病理学图像 网络（GAN），以帮助计算/数字量化和预测表达的蛋白质和 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集成分子和细胞规模的数据并进行分析。  协助准备稿件。