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The performance of these methods in gene expression microarray has been extensively evaluated [ 22].
When compared with standard methods in gene expression analysis, our approach yielded specific results, contrary to agglomerative hierarchical clustering.
The most widely used methods in gene expression signature modelling to date can be partitioned into two broad groups [ 6].
As the use of independence assumption methods in gene array studies is widespread, we conclude that published false gene set findings may be common.
We selected this method as it is the most flexible with the best performance among the commonly used batch correction methods in gene expression microarray [ 22].
The recursive elastic net addresses the drawback of the elastic net and has higher true discovery rate than other competing methods in gene network inference.
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Targeted sequence alteration would be an attractive method in gene therapy and biotechnology.
The use of genomic, non-coding sequence information to further annotate a gene is an important method in gene discovery, especially when protein similarity methods begin to reach their limit.
In this paper, we demonstrate applications of the TREC-IN method in gene complementation and genome minimization studies in Mmc.
This incorporation would further provide higher sensitivity and higher true discovery rate of the proposed method in gene network inference.
We design three simulation studies to examine the performance of the proposed method in gene set enrichment meta-analysis, and compare it with the three existing methods, MAPE_G, MAPE_P and MAPE_I, developed by Shen and Tseng (2010).
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