A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response
The analysis of biogenetic data makes an important contribution to the understanding of disease mechanisms and the diagnosis of rare diseases. In this analysis, the selection of significant features affecting the disease provides an effective basis for subsequent disease judgment and treatment direc...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2024/9014764 |
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| author | Hanji He Jianfeng He Guangming Deng |
| author_facet | Hanji He Jianfeng He Guangming Deng |
| author_sort | Hanji He |
| collection | DOAJ |
| description | The analysis of biogenetic data makes an important contribution to the understanding of disease mechanisms and the diagnosis of rare diseases. In this analysis, the selection of significant features affecting the disease provides an effective basis for subsequent disease judgment and treatment direction. However, this is not a simple task as biogenetic data have challenges such as ultra-high dimensionality of potential features, imbalance of response variables, and genetic associations. This study focuses on the group structure in feature screening with biogenetic data. Specifically, group structure exists for biogenetic data, so we need to analyze the entire genome rather than individual strongly correlated genes. This study proposes a group feature screening method that considers group correlations using adjusted Pearson’s cardinality statistic to address this issue. The method can be applied to both continuous and discrete covariates. The performance of the proposed method is illustrated by simulation studies, where the proposed method performs well with imbalanced data and multicategorical responses. In the application of lung cancer diagnosis, the proposed method for imbalanced data categorization is impressive, and the dimension reduction using linear discriminant is still good. |
| format | Article |
| id | doaj-art-d30addae35ec4108a97aa6b4988790c1 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-d30addae35ec4108a97aa6b4988790c12025-02-03T11:53:34ZengWileyJournal of Mathematics2314-47852024-01-01202410.1155/2024/9014764A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical ResponseHanji He0Jianfeng He1Guangming Deng2School of Mathematics and StatisticsSchool of Economics and FinanceSchool of Mathematics and StatisticsThe analysis of biogenetic data makes an important contribution to the understanding of disease mechanisms and the diagnosis of rare diseases. In this analysis, the selection of significant features affecting the disease provides an effective basis for subsequent disease judgment and treatment direction. However, this is not a simple task as biogenetic data have challenges such as ultra-high dimensionality of potential features, imbalance of response variables, and genetic associations. This study focuses on the group structure in feature screening with biogenetic data. Specifically, group structure exists for biogenetic data, so we need to analyze the entire genome rather than individual strongly correlated genes. This study proposes a group feature screening method that considers group correlations using adjusted Pearson’s cardinality statistic to address this issue. The method can be applied to both continuous and discrete covariates. The performance of the proposed method is illustrated by simulation studies, where the proposed method performs well with imbalanced data and multicategorical responses. In the application of lung cancer diagnosis, the proposed method for imbalanced data categorization is impressive, and the dimension reduction using linear discriminant is still good.http://dx.doi.org/10.1155/2024/9014764 |
| spellingShingle | Hanji He Jianfeng He Guangming Deng A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response Journal of Mathematics |
| title | A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response |
| title_full | A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response |
| title_fullStr | A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response |
| title_full_unstemmed | A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response |
| title_short | A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response |
| title_sort | group feature screening procedure based on pearson chi square statistic for biology data with categorical response |
| url | http://dx.doi.org/10.1155/2024/9014764 |
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