# Adaptive evolutionary inference frameworks for understudied populations using generative neural networks

> **NIH NIH R15** · HAVERFORD COLLEGE · 2024 · $350,141

## Abstract

PROJECT SUMMARY
In population genetics, deep neural networks have largely replaced traditional approaches such
as approximate Bayesian computation (ABC) and other summary statistic-based methods.
However, there has been a lack of exploration and comparison of different architectures and
input representation of genetic data. Additionally, interpreting trained neural networks and their
predictions remains largely unexplored in this field. In this project we will develop new inference
frameworks based on transformers, and compare these approaches with existing methodology.
We will focus on the challenging task of joint inference, where a locus-specific forces such as
natural selection or per site, per haplotype admixture are confounded by demographic history.
We will create interpretability frameworks that allow researchers to understand what deep
learning methods are learning at multiple scales. Local interpretability will explain network
output on the genetic region level, while global interpretability focuses on individual network
components such as convolutional filters or fully connected layers. Our interpretability methods
will allow us to generate not just predictions and parameter estimates, but higher-level scientific
insight for diverse human populations. Taken together, outcomes from our results will guide
researchers toward the best networks for their inference tasks, quantify evolutionary processes
in admixed populations, and illuminate the reasoning behind network predictions.

## Key facts

- **NIH application ID:** 10875841
- **Project number:** 2R15HG011528-02
- **Recipient organization:** HAVERFORD COLLEGE
- **Principal Investigator:** Sara Mathieson
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $350,141
- **Award type:** 2
- **Project period:** 2020-12-18 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10875841

## Citation

> US National Institutes of Health, RePORTER application 10875841, Adaptive evolutionary inference frameworks for understudied populations using generative neural networks (2R15HG011528-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10875841. Licensed CC0.

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