Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/133611
Type: | Thesis |
Title: | A Machine Learning Approach for Detecting Selective Sweeps Using Ancient DNA |
Author: | Kwong, Shing Yan |
Issue Date: | 2021 |
School/Discipline: | School of Mathematical Sciences |
Abstract: | Biological adaptation leads to speci c patterns in population genetic data called selective sweeps. Although researchers have applied machine learning to sweep detection, which speci c methods are appropriate for any given scenario is not well understood. We conducted a systematic review of a suite of machine learning(ML) classi ers for sweep detection. We found that accurate models can be built using simple, fast classi ers supported by preprocessing. We produced a ML work ow which is applicable for general population genetic problems. Our methods were extended for ancient DNA, showing a sweep signal can be retrieved even at high missing rates. |
Advisor: | Tuke, Simon Bean, Nigel Huber, Christian |
Dissertation Note: | Thesis (MPhil) -- University of Adelaide, School of Mathematics, 2021 |
Keywords: | machine learning population genetics selective sweeps evolution |
Provenance: | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals |
Appears in Collections: | Research Theses |
Files in This Item:
File | Description | Size | Format | |
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Kwong2021_MPhil.pdf | 6.26 MB | Adobe PDF | View/Open |
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