Data Mining for Genetic Analysis of Kofendrerd Personality Disorder
Author Information
Author(s): Wei Liang-Ying, Huang Cheng-Lung, Chen Chien-Hsiun
Primary Institution: Academia Sinica, Huafan University, Taipei, Taiwan
Hypothesis
Can rough set theory and decision trees effectively identify genes associated with Kofendrerd Personality Disorder?
Conclusion
The study found that while decision trees accurately predicted the disease trait, they failed to identify true disease-related loci.
Supporting Evidence
- The decision trees had accuracy rates of about 99% in predicting the disease trait.
- Phenotypes b and h were frequently included in the decision trees across groups.
- The decision trees for the NYC group had different structures compared to other groups.
Takeaway
Researchers used special methods to find genes linked to a behavior problem called Kofendrerd Personality Disorder, but they couldn't find the exact genes they were looking for.
Methodology
The study used a two-stage process involving decision trees and rough set theory to analyze simulated genetic data.
Limitations
The methods used may not be suitable for identifying complex genetic associations due to low penetrance rates of disease alleles.
Participant Demographics
Subjects were from four geographically diverse sites with varied criteria for diagnosis of Kofendrerd Personality Disorder.
Digital Object Identifier (DOI)
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