2020
DOI: 10.1101/2020.03.10.985960
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Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Abstract: Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricti… Show more

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“…where y represents the SV trait, is the overall population mean, and e is the model residual µ In contrast to predictions obtained using models trained with the entire set of markers, we explored the feasibility of feature selection (FS) techniques for subsetting SNP data based on putative phenotype-genotype associations. We identified the intersection of at least two out of the three methods established [45][46][47]: (i) the gradient tree boosting (GTB) regressor model, (ii) Pearson correlation (maximum p value of 0.05), and (iii) L1-based FS with a linear support vector regression system (SVM). Using this subset, we assessed the importance of each SNP for prediction by calculating its feature importance with two different tree-based ML algorithms: a decision tree (DT) and a random forest (RF).…”
Section: Snp Prioritization Through Machine Learningmentioning
confidence: 99%
“…where y represents the SV trait, is the overall population mean, and e is the model residual µ In contrast to predictions obtained using models trained with the entire set of markers, we explored the feasibility of feature selection (FS) techniques for subsetting SNP data based on putative phenotype-genotype associations. We identified the intersection of at least two out of the three methods established [45][46][47]: (i) the gradient tree boosting (GTB) regressor model, (ii) Pearson correlation (maximum p value of 0.05), and (iii) L1-based FS with a linear support vector regression system (SVM). Using this subset, we assessed the importance of each SNP for prediction by calculating its feature importance with two different tree-based ML algorithms: a decision tree (DT) and a random forest (RF).…”
Section: Snp Prioritization Through Machine Learningmentioning
confidence: 99%