Application of machine learning methods for mining association rules in plant and animal data sets containing molecular genetic markers, followed by classification or prediction utilizing features created from these association rules

Abstract

The disclosure relates to the use of one or more association rule mining algorithms to mine data sets containing features created from at least one plant or animal-based molecular genetic marker, find association rules and utilize features created from these association rules for classification or prediction.

Description

[0001] priority statement [0002] This application claims priority based on Provisional Application 61 / 221,804 filed June 30, 2009 in the US Patent and Trademark Office, the entire disclosure of which is hereby incorporated by reference. technical field [0003] The present disclosure relates to the use of one or more association rule mining algorithms to mine a data set containing features created by at least one plant- or animal-based molecular genetic marker to find Association rules, and utilizing the features created by these association rules for classification or prediction. Background technique [0004] A major goal of plant and animal improvement is to obtain new cultivars that are superior in desired target traits such as yield, grain oil content, disease resistance and resistance to abiotic stress. [0005] Traditional methods of plant and animal improvement have been to select individual plants or animals based on their phenotype, or the phenotype of their pro...

AI Technical Summary

Problems solved by technology

Multicollinearity can lead to less accurate estimates of the impact of a feature (or subset of features) on a target feature and thus biased predictions

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