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.