# Machine Learning for Integrative Modeling of the Immune System in Clinical Settings

> **NIH NIH R35** · STANFORD UNIVERSITY · 2021 · $96,085

## Abstract

Machine Learning for Integrative Modeling of the Immune System in Clinical Settings
In response to an immunological challenge, immune cells act in concert forming complex and dense networks.
A deep understanding of these immune responses is often the first step in developing immune therapies and
diagnostic tests. Multivariate modeling algorithms can simultaneously consider all measured aspects of the
immune system but requires prohibitively larger cohort sizes as technological advancements increase the
number of measurements (a.k.a., “Curse of Dimensionality”). To address this, we propose a series of studies to
develop machine learning algorithms for comprehensive profiling of the immune system in clinical settings.
Particularly, for analysis of the immune system at a single-cell-level, we will leverage the stochastic nature of
clustering algorithms to produce a robust pipeline for prediction of clinical outcomes. Next, we introduce the
immunological Elastic-Net (iEN) algorithm, which addresses both the curse of dimensionality and reproducibility
by integrating prior immunological knowledge into the models.
 The cellular systems that govern immunity act through symbiotic interactions with multiple interconnected
biological systems. The simultaneous interrogation of these systems with suitable technologies can reveal
otherwise unrecognized crosstalk. In collaboration with several leading laboratories, we have produced
multiomics datasets (including analysis the genome, proteome, microbiome, and metabolome) in synchronized
groups of patients. Using these coordinated datasets, we will evaluate several algorithms for combining multiple
biological modalities while accounting for the intrinsic characteristics of each assay, to reveal biological cross-
talk across various systems and increase combined predictive power. Importantly, numerous population-
level factors (including medical history, environmental, and socioeconomic factors) significantly impact the
immune system and studies focused on homogenous patient populations often lack generalizability to other
populations. To address this, we will develop machine learning strategies to integrate population-level factors
directly into our immunological data. These models will objectively define subpopulations of patients and enable
flexibility in the coefficients of the models (and hence, the importance of the various biological measurements)
in each group.
 This research program will be executed using data from several biorepositories focused on various
diseases. This approach will ensure generalizability of our work to previously unseen datasets and increase the
long-term impact of our findings. Throughout the proposal, a major area of focus is the development of
visualization and model-reduction strategies that lay the foundation for interpretation of complex models. The
machine learning algorithms developed will be readily applicable to a broad range of multiomics and multicohort
studies and will b...

## Key facts

- **NIH application ID:** 10433729
- **Project number:** 3R35GM138353-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Nima Aghaeepour
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $96,085
- **Award type:** 3
- **Project period:** 2020-09-05 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10433729

## Citation

> US National Institutes of Health, RePORTER application 10433729, Machine Learning for Integrative Modeling of the Immune System in Clinical Settings (3R35GM138353-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10433729. Licensed CC0.

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