# Core B: Integrative Data-Science Core

> **NIH NIH P01** · J. DAVID GLADSTONE INSTITUTES · 2023 · $637,663

## Abstract

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.

## Key facts

- **NIH application ID:** 10670335
- **Project number:** 5P01AG073082-03
- **Recipient organization:** J. DAVID GLADSTONE INSTITUTES
- **Principal Investigator:** KATHERINE S. POLLARD
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $637,663
- **Award type:** 5
- **Project period:** 2021-08-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10670335

## Citation

> US National Institutes of Health, RePORTER application 10670335, Core B: Integrative Data-Science Core (5P01AG073082-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10670335. Licensed CC0.

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