ArticlePloS one2012
A new avenue for classification and prediction of olive cultivars using supervised and unsupervised algorithms.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- High-throughput olive germplasm classification using morphological phenotyping and machine learning.Scientific reports · 2026Article
- Evaluation of Lens culinaris germplasm against ascochyta blight by applying phenotyping and defense genes attributes.Scientific reports · 2025Article
- Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enrichedHeliyon · 2025Article
- Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization.Biochemistry and biophysics reports · 2024Article
- Machine Learning-Based Identification of Mating Type and Metalaxyl Response inMicroorganisms · 2024Article
- Discrimination of 14 olive cultivars using morphological analysis and machine learning algorithms.Frontiers in plant science · 2024Article
- Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury.CNS neuroscience & therapeutics · 2023Article
- Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp.Life (Basel, Switzerland) · 2022Article
- Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.PloS one · 2021Article
- Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.PloS one · 2021Article
- Characterization of bovine (Bos taurus) imprinted genes from genomic to amino acid attributes by data mining approaches.PloS one · 2019Article
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- Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis.Physiology and molecular biology of plants : an international journal of functional plant biology · 2016Article
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- Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms.BMC research notes · 2014Article
- Prediction of lung tumor types based on protein attributes by machine learning algorithms.SpringerPlus · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various methods have been used to identify cultivares of olive trees; herein we used different bioinformatics algorithms to propose new tools to classify 10 cultivares of olive based on RAPD and ISSR genetic markers datasets generated from PCR reactions. Five RAPD markers (OPA0a21, OPD16a, OP01a1, OPD16a1 and OPA0a8) and five ISSR markers (UBC841a4, UBC868a7, UBC841a14, U12BC807a and UBC810a13) selected as the most important markers by all attribute weighting models. K-Medoids unsupervised clustering run on SVM dataset was fully able to cluster each olive cultivar to the right classes. All trees (176) induced by decision tree models generated meaningful trees and UBC841a4 attribute clearly distinguished between foreign and domestic olive cultivars with 100% accuracy. Predictive machine learning algorithms (SVM and Naïve Bayes) were also able to predict the right class of olive cultivares with 100% accuracy. For the first time, our results showed data mining techniques can be effectively used to distinguish between plant cultivares and proposed machine learning based systems in this study can predict new olive cultivars with the best possible accuracy.
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