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- W3022389930 abstract "Abstract Background Feature screening plays a critical role in handling ultrahigh dimensional data analyses when the number of features exponentially exceeds the number of observations. It is increasingly common in biomedical research to have case-control (binary) response and an extremely large-scale categorical features. However, the approach considering such data types is limited in extant literature. In this article, we propose a new feature screening approach based on the iterative trend correlation (ITC-SIS, for short) to detect important susceptibility loci that are associated with the polycystic ovary syndrome (PCOS) affection status by screening 731,442 SNP features that were collected from the genome-wide association studies. Results We prove that the trend correlation based screening approach satisfies the theoretical strong screening consistency property under a set of reasonable conditions, which provides an appealing theoretical support for its outperformance. We demonstrate that the finite sample performance of ITC-SIS is accurate and fast through various simulation designs. Conclusion ITC-SIS serves as a good alternative method to detect disease susceptibility loci for clinic genomic data." @default.
- W3022389930 created "2020-05-13" @default.
- W3022389930 creator A5039100338 @default.
- W3022389930 creator A5042049924 @default.
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- W3022389930 date "2020-05-04" @default.
- W3022389930 modified "2023-10-10" @default.
- W3022389930 title "Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening" @default.
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- W3022389930 doi "https://doi.org/10.1186/s12859-020-3492-z" @default.
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