# Causal Effect Estimation of Regulatory Molecules

> **NIH NIH R00** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $249,000

## Abstract

Project Summary/Abstract
Transcription factors and microRNAs are essential regulatory molecules (RM) that control messenger RNAs
(mRNA) and are known to be dysregulated in human diseases. Each RM may affect multiple pathways of the
cell which is both a blessing and a curse. If a therapy targets the proper RM, it can attack the disease from
multiple fronts and increase efficacy. On the other hand, targeted therapy may result in serious adverse effects
due to our limited knowledge of the downstream causal effect of RM manipulation. Although the local bindings
between single RMs and their targets have been studied computationally and experimentally, the intensity of
functional consequences of such bindings on the transcriptome is unclear. Here, I propose statistical machine
learning techniques and causal inference methods to predict the observed variability of gene expression
using only regulatory molecules and estimate their downstream causal effect on the entire
transcriptome. To achieve this goal, I start in Aim 1 by building a multi-response predictive model to predict
the whole transcriptome using only RMs. This goal is challenging because the dimension of the response vector
is more than the number of samples and I will use techniques from high-dimensional statistics to address this
issue. In Aim 2, I will go beyond predictive modeling by estimating the causal effect of RMs on the transcriptome
using invariant causal prediction. I will leverage the rapidly growing literature which connects causal inference
to invariant prediction accuracy across heterogeneous data sources to infer the causal effect of RMs on mRNA.
Having developed both predictive and causal models of RMs contribution to gene regulation, in Aim 3 during the
R00 phase, I will focus on the most recent advances in double/debiased machine learning which allows the
use of scalable machine learning methods for reliable estimation of causal effect of RMs on transcription. My
proposed research will bring the most advanced statistical machine learning and causal inference techniques to
genomics research and help design more effective targeted therapies by providing insights into the global role
of RMs in gene expression regulation. During the training phase of the award, with the support of my outstanding
mentoring team and scientific advisory committee, I will gain expertise in molecular biology and genomics while
perfecting my knowledge of causal inference and machine learning. The Ohio State University Comprehensive
Cancer Center – James Hospital and the Mathematical Biosciences Institute will provide me with the ideal
interdisciplinary environment to bridge data science and genomics and will help me achieve my career
development goals and transition to a tenure-track faculty position.

## Key facts

- **NIH application ID:** 10455118
- **Project number:** 4R00HG011367-02
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Amir Asiaeetaheri
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $249,000
- **Award type:** 4N
- **Project period:** 2021-08-06 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10455118

## Citation

> US National Institutes of Health, RePORTER application 10455118, Causal Effect Estimation of Regulatory Molecules (4R00HG011367-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10455118. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*