UPGMA and artificial neural networks applications on wild type olives

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SESLİ M., YEĞENOĞLU E. D., ALTINTAŞ V., Gevrekçi Y.

Journal of Environmental Biology, cilt.38, sa.5, ss.1079-1084, 2017 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 5
  • Basım Tarihi: 2017
  • Doi Numarası: 10.22438/jeb/38/5(si)/gm-26
  • Dergi Adı: Journal of Environmental Biology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1079-1084
  • Anahtar Kelimeler: Artificial neural networks, ISSR, UPGMA, Wild olives
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

Aim: Plant genetic sources are important to study genetic variability and richness of hereditary knowledge of plant species in gene pool. Local varieties, rural populations, wild types and old varieties are the primary ones. In this respect, wild type olives (Olea europaea oleaster) are valuable in terms of olive breeding, cultivation and ecosystem. The aim of the study was to determine genetic distances between olive varieties. Methodology: Artificial Neural Networks intuitive algorithm application was performed on seven wild type olives grown in different regions of Turkey by using data obtained from twenty-two ISSR primers. Results: UPGMA dendrograms were developed through Jaccard, simple matching coefficients, and similarity matrices; and genetic similarities and dissimilarities were exhibited. Interpretation: It was concluded that Artificial Neural Networks would be beneficial for estimating olive types accurately based on the results obtained from earlier studies performed with genetic markers.