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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：
10328212
负责人：
David Gjertson
金额：
$ 21.37万
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/10328212
关键词：
Adopted Allogenic Animals Architecture Area Automobile Driving Back Biological Assay Biometry Biostatistics Core California Cells Clinical Clinical Research Complex Computer Analysis Computer Models Custom 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 Los Angeles Machine Learning Manuscripts Meditation Methodology Methods Modeling Molecular Monitor Mus Myeloid Cells Noise Outcome Pathologic Pathway Analysis Pathway interactions Phase Phenotype 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 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 machine learning method machine vision mouse model multimodal data network models neural network novel 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.

项目总结