The PRFE model of gene expression data used for gene identification.

<p>The PRFE model of gene expression data used for gene identification.</p

Identification accuracies of the five methods on simulation data with different parameters, where <i>p</i>_<i>S</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>L</i> = 1 to test the performance of different <i>p</i>_<i>S</i> values; <i>p</i>_<i>L</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>S</i> = 1 to test the performance of different <i>p</i>_<i>L</i> values; <i>α</i>₁, <i>α</i>₂ and <i>γ</i> are the control-sparsity parameters of PMD, CIPMD and SPCA, respectively.

<p>Identification accuracies of the five methods on simulation data with different parameters, where <i>p</i>_<i>S</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>L</i> = 1 to test the performance of different <i>p</i>_<i>S</i> values; <i>p</i>_<i>L</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>S</i> = 1 to test the performance of different <i>p</i>_<i>L</i> values; <i>α</i>₁, <i>α</i>₂ and <i>γ</i> are the control-sparsity parameters of PMD, CIPMD and SPCA, respectively.</p

Venn diagram of five methods on leukemia data.

<p>Venn diagram of five methods on leukemia data.</p

Identification accuracies of the five methods on simulation data with different samples.

<p>Identification accuracies of the five methods on simulation data with different samples.</p

The detailed information of the 5 'unique' genes identified by PRFE.

<p>The detailed information of the 5 'unique' genes identified by PRFE.</p

AUC statistics for simulation data.

<p>AUC statistics for simulation data.</p

ROC curve for simulation data.

<p>ROC curve for simulation data.</p

The sample number of each stress type in the raw data.

<p>The sample number of each stress type in the raw data.</p

The detailed information of the 30 genes identified by PRFE.

<p>The detailed information of the 30 genes identified by PRFE.</p

A Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest

<div><p>The functional region of interest (fROI) approach has increasingly become a favored methodology in functional magnetic resonance imaging (fMRI) because it can circumvent inter-subject anatomical and functional variability, and thus increase the sensitivity and functional resolution of fMRI analyses. The standard fROI method requires human experts to meticulously examine and identify subject-specific fROIs within activation clusters. This process is time-consuming and heavily dependent on experts’ knowledge. Several algorithmic approaches have been proposed for identifying subject-specific fROIs; however, these approaches cannot easily incorporate prior knowledge of inter-subject variability. In the present study, we improved the multi-atlas labeling approach for defining subject-specific fROIs. In particular, we used a classifier-based atlas-encoding scheme and an atlas selection procedure to account for the large spatial variability across subjects. Using a functional atlas database for face recognition, we showed that with these two features, our approach efficiently circumvented inter-subject anatomical and functional variability and thus improved labeling accuracy. Moreover, in comparison with a single-atlas approach, our multi-atlas labeling approach showed better performance in identifying subject-specific fROIs.</p></div