Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations
[Motivation] Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness).
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| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Oxford University Press
2015-12-15
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| Online Access: | http://hdl.handle.net/10261/151773 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100002809 http://dx.doi.org/10.13039/501100004587 |
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| Summary: | [Motivation] Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness). |
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