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

> **NIH NIH R15** · HAVERFORD COLLEGE · 2021 · $432,494

## Abstract

PROJECT SUMMARY
In the field of population genetics, machine learning methods are emerging as promising
frameworks for understanding evolution. However, these algorithms rely heavily on simulated
datasets, which currently fail to recapitulate the features of diverse natural genomes. Deep
neural networks in particular are disconnected from evolutionary modeling, and their results are
difficult to interpret in a biological context. In this project, we propose to develop simulation
frameworks that automatically adapt to any population or species. The resulting customized
synthetic datasets will be used to train neural networks that quantify the unique evolutionary
histories of understudied human groups. By including genealogical and epigenetic information
as auxiliary input, we will be able to link predictions back to genomic features. Our results will
enable us to estimate the interactions between local phenomena such as natural selection,
mutation patterns, and recombination hotspots. Taken together, outcomes from our work will
allow us to create a detailed model evolutionary of processes, both along the genome and
across human populations.

## Key facts

- **NIH application ID:** 10114449
- **Project number:** 1R15HG011528-01
- **Recipient organization:** HAVERFORD COLLEGE
- **Principal Investigator:** Sara Mathieson
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $432,494
- **Award type:** 1
- **Project period:** 2020-12-18 → 2024-06-06

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10114449

## Citation

> US National Institutes of Health, RePORTER application 10114449, Adaptive evolutionary inference frameworks for understudied populations using generative neural networks (1R15HG011528-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10114449. Licensed CC0.

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