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Classification and prediction of three and multi stigma in saffron by statistical, unsupervised machine learning tools

Zirā̒at va Fanāvarī-i Za̒farān, 2014-10, Vol.2 (3), p.199-204 [Peer Reviewed Journal]

ISSN: 2383-1529 ;EISSN: 2383-2142 ;DOI: 10.22048/jsat.2014.7810

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  • Title:
    Classification and prediction of three and multi stigma in saffron by statistical, unsupervised machine learning tools
  • Author: Amir Hosein Beiki
  • Subjects: Classifier ; Machine Learning ; Molecular marker ; Sequence-related amplified polymorphism
  • Is Part Of: Zirā̒at va Fanāvarī-i Za̒farān, 2014-10, Vol.2 (3), p.199-204
  • Description: Saffron is a triploid, sterile plant, used as a spice and medicinalplant in all countries. Stigma is the most important part of saffron. So far no reliable molecular methods were provided to identify and prediction of the three/multi branches species. In this study, using different bioinformatics algorithms, new tools for prediction based on Sequence-Related Amplified Polymorphismmolecular markers is presented. Five alleles M1311400, M151200, M12100 and M10850 selected as the most important classifier by Attribute Weighting models which has the potential to cluster and recognize the three from multi branches stigma. K-Means and K-Medoids unsupervised clustering algorithms were fully able to cluster each genotype to the right classes. Our results showed that for the first time, data mining techniques can be effectively used to genetic differentiation between three and multi stigma with above 90 percent the accuracy andprecision. These methods can use in gene mapping and selection by biomarker.
  • Publisher: University of Torbat Heydarieh
  • Language: Persian
  • Identifier: ISSN: 2383-1529
    EISSN: 2383-2142
    DOI: 10.22048/jsat.2014.7810
  • Source: DOAJ Directory of Open Access Journals
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