title: Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.
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.
authors.affiliation: National Institute of Genetic Engineering & Biotechnology, Tehran, Iran.
authors.forename: Bahareh
authors.initials: B
authors.lastname: Torkzaban
authors.affiliation: Department of Biology, School of Basic Science, University of Qom, Qom, Iran.
authors.forename: Amir Hossein
authors.initials: AH
authors.lastname: Kayvanjoo
authors.affiliation: National Institute of Genetic Engineering & Biotechnology, Tehran, Iran.|Department of Gene Technology, KTH, Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden.
authors.forename: Arman
authors.initials: A
authors.lastname: Ardalan
authors.affiliation: National Institute of Genetic Engineering & Biotechnology, Tehran, Iran.
authors.forename: Soraya
authors.initials: S
authors.lastname: Mousavi
authors.affiliation: CNR, Institute of Biosciences & Bioresources, Perugia, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Mariotti
authors.affiliation: CNR, Institute of Biosciences & Bioresources, Perugia, Italy.
authors.forename: Luciana
authors.initials: L
authors.lastname: Baldoni
authors.affiliation: Institute of Biotechnology, College of Agriculture, Shiraz University, Shiraz, Iran.|Department of Genetics and Evolution, School of Biological Sciences, University of Adelaide, Adelaide, Australia.|School of Information Technology and Mathematical Sciences, Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, Australia.|School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, Australia.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Ebrahimie
authors.affiliation: Department of Biology, School of Basic Science, University of Qom, Qom, Iran.
authors.forename: Mansour
authors.initials: M
authors.lastname: Ebrahimi
authors.affiliation: National Institute of Genetic Engineering & Biotechnology, Tehran, Iran.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Hosseini-Mazinani
mesh_terms: D000465:Algorithms; D000483:Alleles; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D018744:DNA, Plant / Q000235:genetics; D003663:Decision Trees; D017343:Genes, Plant; D014644:Genetic Variation; D005838:Genotype; D005843:Geography; D007492:Iran; D000069550:Machine Learning; D018895:Microsatellite Repeats; D031658:Olea / Q000235:genetics*; D058974:Phylogeography; D015203:Reproducibility of Results
chemical_list: D018744:DNA, Plant