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Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on(More)
Neural networks have been massively used in regression problems due to their ability to approximate complex nonlinear mappings directly from input patterns. However, collected data for training networks often include outliers which affect final results. This paper presents an approach for training single hidden-layer feedforward neural networks (SLFNs)(More)
Despite having a relatively low incidence, renal cell carcinoma (RCC) is one of the most lethal urologic cancers. For successful treatment including surgery, early detection is essential. Currently there is no screening method such as biomarker assays for early diagnosis of RCC. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry(More)
Ethanol production using hemicelluloses has recently become a focus of many researchers. In order to promote D: -xylose fermentation, we cloned the bacterial xylA gene encoding for xylose isomerase with 434 amino acid residues from Agrobacterium tumefaciens, and successfully expressed it in Saccharomyces cerevisiae, a non-xylose assimilating yeast. The(More)
A shared-weight neural network based on mathematical morphology is introduced. The feature extraction process is learned by interaction with the classification process. Feature extraction is performed using gray-scale hit-miss transforms that are independent of gray-level shifts. The morphological shared-weight neural network (MSNN) is applied to automatic(More)
Clonorchis sinensis, the parasite that causes clonorchiasis, is endemic in many Asian countries, and infection with the organism drives changes in the liver tissues of the host. However, information regarding the molecular events in clonorchiasis remains limited, and little is currently known about host–pathogen interactions in clonorchiasis. In this study,(More)
Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden(More)