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Core B: Integrative Data-Science Core

核心 B：综合数据科学核心

基本信息

批准号：
10670335
负责人：
KATHERINE S. POLLARD
金额：
$ 63.77万
依托单位：
J. DAVID GLADSTONE INSTITUTES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10670335
关键词：
Address Affect Alzheimer&apos s Disease Alzheimer&apos s disease model Amyloid beta-Protein Apolipoprotein E Apolipoproteins Atlases Attention Autopsy Behavioral Behavioral Assay Bioinformatics Biological Assay Biology Brain Cell Nucleus Cells Cellular Assay Code Communities Complex Coupled Data Data Analyses Data Collection Data Display Data Science Core Data Set Disease Disease model Electrophysiology (science)Etiology Experimental Designs Gene Expression Gene Expression Regulation Generations Genes Genetic Genetic Predisposition to Disease Genomics Genotype Goals Human Impaired cognition Inherited Intuition Lead Libraries Link Machine Learning Measures Modeling Molecular Mus Neurons Pathologic Pathology Pattern Phenotype Procedures Process Program Research Project Grants Protocols documentation Public Domains Regulator Genes Research Research Design Resources Sampling Source Code Standardization Statistical Models Synapses System Systems Development Techniques Testing Time Tissue Donors Tissues Variant Visualization brain tissue cell type cohort computerized tools data exploration data integration data portal deep learning design diverse data epigenome experimental study genetic variant genomic data high dimensionality humanized mouse improved innovation large scale data machine learning method mouse model multimodality network dysfunction network models neural network neuropathology novel open source phenotypic data power analysis predictive modeling programs response sequencing platform single nucleus RNA-sequencing statistics tau Proteins tool transcriptome transcriptomics user-friendly web portal web site

项目摘要

CORE B – ABSTRACT This program aims to discover the molecular drivers and consequences of network dysfunction in Alzheimer’s disease (AD) through rigorous characterization of cell-type specific gene regulation and multi-modal phenotypes. We will use human samples and a variety of mouse models. This breadth and depth of data across different organismal and cellular contexts present a unique opportunity for integrative modeling. To capitalize on this opportunity, however, the data must be quantitatively comparable across projects. To address this challenge, the Integrative Data-Science Core (Core B) will use the “design for inference” approach, which means that the predictive modeling and hypothesis testing we plan to do will guide all stages of experimental design. To minimize and correct batch effects, we will standardize experimental protocols and establish a repeated-measures experimental design, which will boost the power for analyses. A second challenge is how to summarize and jointly model complex, high-dimensional phenotypes with single-cell and single-nucleus transcriptomic profiles. To solve this problem, we will develop innovative machine-learning and network models, with a focus on deep learning and sparse canonical correlation analysis to extract information from multivariate data and discover relationships between pairs of data types. To facilitate real-time sharing of results and exploration of data across projects, we will implement data tracking systems, Jupyter notebooks with pipelines and analytical code, and an interactive data portal with visualization and query capabilities. These collaborative tools will also help us share our data, code, and results rapidly with the AD research community through our Synapse website. Collectively, the activities of Core B will provide cutting-edge computational support to all four projects, enable cross-project discovery, and set new standards for the use of large-scale data integration to decipher molecular mechanisms in AD and other diseases.

核心b -抽象