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

A technology of genetic markers and machine learning, applied in the fields of botanical equipment and methods, applications, bioinformatics, etc., can solve the problem of low accuracy

Inactive Publication Date: 2012-05-23
DOW AGROSCIENCES LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Multicollinearity can lead to less accurate estimates of the impact of a fea

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  • 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
  • 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
  • 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

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Embodiment

[0280] The following examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure.

[0281]Field and greenhouse screens are used to identify elite maize lines containing high and low levels of resistance to pathogens. Lines showing high levels of resistance to the pathogen are used as donors and crossed with susceptible elite lines. The progeny are then backcrossed to the same susceptible elite lines. The resulting population was crossed with haploid inducer stock and 191 fixed inbred lines were developed using chromosome doubling techniques. The level of resistance of each line to the pathogen was assessed in two replicates using field screening methodology. Forty-four replicates of susceptible elite lines were also evaluated using field screening methods. Genotype data were generated using 93 polymorphic SSR markers for all 191 doubled haploid lines, susceptible elite lines and resistant donors.

[0282] The final dataset ...

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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...

Claims

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Application Information

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IPC IPC(8): G06N5/02G16B20/20
CPCG06N5/025G16B20/00G16B20/20A01H1/04
Inventor D.卡拉维洛R.帕特尔R.佩
Owner DOW AGROSCIENCES LLC
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