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Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population...
Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population...
Diffusion tensor imaging (DTI) using a combination of direct anisotropy measurements provided a more anatomically accurate morphological representation of the human spinal cord than traditional anisotropy indices. Furthermore, the use of a fuzzy logic algorithm to segment regions of gray and white matter within the spinal cord based on these anisotropy measurements allowed for morphometric analyses...
Diffusion tensor imaging (DTI) using a combination of direct anisotropy measurements provided a more anatomically accurate morphological representation of the human spinal cord than traditional anisotropy indices. Furthermore, the use of a fuzzy logic algorithm to segment regions of gray and white matter within the spinal cord based on these anisotropy measurements allowed for morphometric analyses...
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then...
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then...
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