Department of Orthopaedic Surgery Publications (UMKC)
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Items in this collection are the scholarly output of the Department of Orthopaedic Surgery faculty, staff, and students, either alone or as co-authors, and which may or may not have been published in an alternate format.
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Item Gene selection for classification of microarray data based on the Bayes error(2007-10-03) Zhang, Ji-Gang; Deng, Hong-WenAbstract Background With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy. Results In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes. Conclusion The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.Item HAPSIMU: a genetic simulation platform for population-based association studies(2008-08-05) Zhang, Feng; Liu, Jianfeng; Chen, Jie; Deng, Hong-WenAbstract Background Population structure is an important cause leading to inconsistent results in population-based association studies (PBAS) of human diseases. Various statistical methods have been proposed to reduce the negative impact of population structure on PBAS. Due to lack of structural information in real populations, it is difficult to evaluate the impact of population structure on PBAS in real populations. Results We developed a genetic simulation platform, HAPSIMU, based on real haplotype data from the HapMap ENCODE project. This platform can simulate heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model. Moreover, both qualitative and quantitative traits can be simulated using additive genetic model with various genetic parameters designated by users. Conclusion HAPSIMU provides a common genetic simulation platform to evaluate the impact of population structure on PBAS, and compare the relative performance of various population structure identification and PBAS methods.