Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

Drought is one of the prime abiotic stresses in the world. Now, amongst the new technologies available for speed up the releasing of new drought tolerance genotypes, there is an emanate discipline called machine learning. The study presents Machine Learning for identification, classification and prediction of drought tolerance maize inbred lines based on SSR genetic markers datasets generated from PCR reactions. A total of 356 SSR reproducible fragment alleles were detected across the 71 polymorphic SSR loci. A dataset of 12 inbred lines with these fragments prepared as attributes and was imported into RapidMiner software. After removal of duplicates, useless and correlated features, 311 feature attributes were polymorphic, ranging in size from 1500 to 3500 bp. The most important attribute fragment alleles in different attribute weighting selected. Ten datasets created using attribute selection (weighting) algorithms. Different classification algorithms were applied on datasets. These can be used to identify groups of alleles with similar patterns of expression, and are able to create some models that have been applied successfully in the prediction, classification and pattern recognition in drought stress. Some unsupervised models were able to differentiate tolerant inbred lines from susceptible. Four unsupervised models were able to produce the different decision trees with root and leaves. The most important attribute alleles almost in all of models were phi033a3, bnlg1347a1 and bnlg172a2 respectively, that can help to identify tolerant maize inbred lines with high precision.