Figure 1 | Scientific Reports

Figure 1

From: Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Figure 1

A schematic workflow of the procedures adopted to identify the underlaying heterotic grouping pattern in the studied germplasm pool of sunflower. The input dataset obtained was from the combination of phenotypic (9 morphological traits); genotypic (binary data obtained from 40 SSR markers scoring); proteomic (binary data from SDS-PAGE based characterization). The dataset was then scaled to the values ranging between 0 and 1 to remove data biasedness. Three machine learning based classifiers were then tested to produce the heterotic grouping in the studied sunflower materials. Out of three, Hierarchical clustering showed the maximum resolution power in terms of correctly classifying the A, B, R and SFP lines. Hierarchical clustering classified the 109 sunflower genotypes in 12 heterotic groups, and this clustering pattern was then used to develop F1 hybrids, while selecting 1 sunflower line from each heterotic group. Both hierarchical and Knn based machine learning clustering have been widely used in plants dataset (including phenotypic and genotypic data) for classification of plant genotypes.

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