Genetic diversity analysis of sesame – A bayesian clustering approach
| Autor: | M. R. Duraisamy, R. Nivedha, S. Manonmani, Patil Santosh Ganapathi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Bayesian probability
Soil Science Plant Science Biology lcsh:Plant culture Machine learning computer.software_genre Clustering Feature (machine learning) lcsh:SB1-1110 Plant breeding Cluster analysis R software Sesame Genetic diversity business.industry fungi food and beverages Diversity analysis Hierarchical clustering Bayesian hierarchical clustering Approximate inference Artificial intelligence business Agronomy and Crop Science computer human activities Diversity (business) |
| Zdroj: | Electronic Journal of Plant Breeding, Vol 10, Iss 2, Pp 748-753 (2019) |
| Popis: | Diversity in plant genetic resources (PGR) provides opportunity for plant breeders to develop new and improved cultivars with desirable characteristics viz., high yield, pest and disease resistance, photosensitivity and high oil quality. Genetic diversity is a ubiquitous feature of all species in nature. Therefore, different genotypes of sesame were used for diversity analysis. Different clustering techniques were widely used for the analysis of diversity. In this paper, Bayesian hierarchical clustering algorithm is applied which can be interpreted as a novel fast bottom-up approximate inference method. Finally, this method clusters the genotypes into various groups with their corresponding genotypes in respective clusters. |
| Databáze: | OpenAIRE |
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