Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

Ziyi Mo; Adam Siepel

doi:10.1371/journal.pgen.1011032

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Open Access

Peer-reviewed

Research Article

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

Ziyi Mo,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America

https://orcid.org/0000-0002-4567-6720

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Adam Siepel

Roles Conceptualization, Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: asiepel@cshl.edu

Affiliations Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America

https://orcid.org/0000-0002-3557-7219

⨯

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

Ziyi Mo,
Adam Siepel

Published: November 7, 2023
https://doi.org/10.1371/journal.pgen.1011032

About the Authors

Ziyi Mo

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America

https://orcid.org/0000-0002-4567-6720

Adam Siepel

Roles Conceptualization, Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: asiepel@cshl.edu

https://orcid.org/0000-0002-3557-7219

Competing Interests

The authors have declared that no competing interests exist.

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Simulation and modeling
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Population genetics
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Neural networks
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Supervised machine learning
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Genomics
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Gene targeting
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Evolutionary adaptation
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Machine learning
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