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BACKGROUND One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the(More)
One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft(More)
BACKGROUND Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first(More)
Reinforcing effects of addictive drugs can be evaluated with the conditioned place preference (CPP) test which involves both the action of drugs and environmental cues. However, the encoded neural circuits and underlying signaling mechanism are not fully understood. In this study, we have used morphine-CPP model in the rat and characterized the role of(More)
The CA3 area serves a key relay on the tri-synaptic loop of the hippocampal formation which supports multiple forms of mnemonic processing, especially spatial learning and memory. To date, morphometric data about human CA3 pyramidal neurons are relatively rare, with little information available for their pre- and postnatal development. Herein, we report a(More)
We report the first experimental observation of two-dimensional surface solitons at the boundaries (edges or corners) of a finite optically induced photonic lattice. Both in-phase and gap nonlinear surface self-trapped states were observed under single-site excitation conditions. Our experimental results are in good agreement with theoretical predictions.
Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection(More)
We report the first theoretical prediction and experimental demonstration of gap soliton trains in a self-defocusing photonic lattice. Without a priori spectral or phase engineering, a stripe beam whose spatial power spectrum lies only in one transverse direction evolves into a gap soliton train with power spectrum growing also in the orthogonal direction(More)
We present a method for She classification of cancer based on gene expression profiles using single genes. We select the genes with high class-discrimination capability according to their depended degree by the classes. We then build classifiers based on the decision rules induced by single genes selected. We test our single-gene classification method on(More)