Mo, Ziyi, Siepel, Adam (November 2023) Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. PLoS Genetics, 19 (11). e1011032. ISSN 1553-7390
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Abstract
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > computational biology > algorithms bioinformatics > computational biology bioinformatics > computational biology > algorithms > machine learning organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks |
| CSHL Authors: | |
| Communities: | CSHL labs > Siepel lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 7 November 2023 |
| Date Deposited: | 20 Nov 2023 14:12 |
| Last Modified: | 20 Nov 2023 14:12 |
| PMCID: | PMC10655966 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41314 |
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