MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction.
BACKGROUND: Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3827354?pdf=render |
| _version_ | 1811274036824506368 |
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| author | Cheng-Hong Yang Yu-Da Lin Li-Yeh Chuang Jin-Bor Chen Hsueh-Wei Chang |
| author_facet | Cheng-Hong Yang Yu-Da Lin Li-Yeh Chuang Jin-Bor Chen Hsueh-Wei Chang |
| author_sort | Cheng-Hong Yang |
| collection | DOAJ |
| description | BACKGROUND: Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1:4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR. CONCLUSIONS/SIGNIFICANCE: Results showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets. |
| first_indexed | 2024-04-12T23:10:34Z |
| format | Article |
| id | doaj.art-72993a178a554244a12d66dfeaa9c7ad |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T23:10:34Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-72993a178a554244a12d66dfeaa9c7ad2022-12-22T03:12:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01811e7938710.1371/journal.pone.0079387MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction.Cheng-Hong YangYu-Da LinLi-Yeh ChuangJin-Bor ChenHsueh-Wei ChangBACKGROUND: Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1:4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR. CONCLUSIONS/SIGNIFICANCE: Results showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets.http://europepmc.org/articles/PMC3827354?pdf=render |
| spellingShingle | Cheng-Hong Yang Yu-Da Lin Li-Yeh Chuang Jin-Bor Chen Hsueh-Wei Chang MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. PLoS ONE |
| title | MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. |
| title_full | MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. |
| title_fullStr | MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. |
| title_full_unstemmed | MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. |
| title_short | MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. |
| title_sort | mdr er balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor dimensionality reduction |
| url | http://europepmc.org/articles/PMC3827354?pdf=render |
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