Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/97201
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Type: Journal article
Title: Machine learning based classification of microsatellite variation: an effective approach for phylogeographic characterization of olive populations
Author: Torkzaban, B.
Kayvanjoo, A.
Ardalan, A.
Mousavi, S.
Mariotti, R.
Baldoni, L.
Ebrahimie, E.
Ebrahimi, M.
Hosseini-Mazinani, M.
Citation: PLoS One, 2015; 10(11):e0143465-1-e0143465-17
Publisher: Public Library of Science
Issue Date: 2015
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
Bahareh Torkzaban, Amir Hossein Kayvanjoo, Arman Ardalan, Soraya Mousavi, Roberto Mariotti, Luciana Baldoni, Esmaeil Ebrahimie, Mansour Ebrahimi, Mehdi Hosseini-Mazinani
Abstract: Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.
Keywords: Olea
DNA, Plant
Bayes Theorem
Reproducibility of Results
Computational Biology
Microsatellite Repeats
Genotype
Alleles
Genes, Plant
Geography
Algorithms
Decision Trees
Iran
Genetic Variation
Phylogeography
Machine Learning
Rights: © 2015 Torkzaban et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
DOI: 10.1371/journal.pone.0143465
Appears in Collections:Aurora harvest 3
Molecular and Biomedical Science publications

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