Generate better data for population genetics using generative adversarial nets

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2020
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Haverford College. Department of Computer Science
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Thesis
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Award
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eng
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Tri-College users only until 2021-01-01, afterwards Open Access
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Abstract
For the past years, researches in population genetics, a subfield of biology studying the variation of genes in a population with respect to space and time, rely heavily on simulated data. That is to say, analysis of the relationship between gene patterns and evolutionary parameters (ie. mutation rate, recombination rate, etc.) is based on simulated data, usually generated using softwares such as msprime. However, the problem with simulated data is that we?re not sure how well it can match the real data as these data are simulated with parameters configured by human beings. Furthermore, this methodology might have a greater effect on machine learning approaches, as our algorithms are trained on these simulated datasets. Under such a circumstance, it is necessary to find out a way so that we can generate good data for research purposes. In this paper, we present some basics about machine learning, and some possible approaches to fulfill our goal using generative adversarial nets (GANs).
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