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Evolutionary sparse learning reveals the shared genetic basis of convergent traits

Allard, John B., Sharma, Sudip, Patel, Ravi, Sanderford, Maxwell, Tamura, Koichiro, Vucetic, Slobodan, Gerhard, Glenn S., Kumar, Sudhir·2025·Nature Communications
DOI 10.1038/s41467-025-58428-8PMC PMC11971283
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  • This combined approach may prove beneficial, as the more computationally intensive models will only need to be applied to the subset of genetic elements identified by the computationally efficient ESL, provided ESL is not overly inaccurate in its initial identification of candidate genetic elements.

    “This approach may prove beneficial, as the more computationally intensive models will only need to be applied to the subset of genetic elements identified by the computationally efficient ESL (provided ESL is not overly inaccurate in its initial identification of candidate genetic elements) (Allard et al. 2025).”

    From From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions (2025) · cites this paper as Allard et al. 2025

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Metadata

Authors
Allard, J. B., Sharma, S., Patel, R., Sanderford, M., Tamura, K., Vucetic, S., Gerhard, G. S., & Kumar, S.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2025-04-04
Volume
16
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-025-58428-8
PMC
PMC11971283
Cited by
11
References
4
Language
en
Licence
Funders
  • U.S. Department of Health & Human Services | National Institutes of Health (GM139540-05)
  • National Academies of Sciences, Engineering, and Medicine | National Academy of Medicine
  • United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Laboratory (W911NF-16-2-0189)
  • Temple University

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