Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.
- DOI
- 10.1186/s12864-026-13163-2
- Published
- 2026 Sep 7
- Container
- BMC genomics
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1186/s12864-026-13163-2,
title = {Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.},
author = {Caliendo C and Gerber S and Pfenninger M},
year = {2026},
journal = {BMC genomics},
doi = {10.1186/s12864-026-13163-2},
url = {https://doi.org/10.1186/s12864-026-13163-2}
}RIS
TY - JOUR TI - Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data. AU - Caliendo C AU - Gerber S AU - Pfenninger M PY - 2026 JO - BMC genomics DO - 10.1186/s12864-026-13163-2 UR - https://doi.org/10.1186/s12864-026-13163-2 ER -
APA
C, C., S, G., & M, P. (2026). Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.. BMC genomics. https://doi.org/10.1186/s12864-026-13163-2
Source records
- pubmed · retrieved 2026-09-26T04:54:30.038Z