RESUMEN
In order to address issues such as the decline in diagnostic performance of deep learning models due to imbalanced data distribution in psoriasis vulgaris,a VGG13-based deep convolutional neural network model is proposed by integrating the processing capability of the improved fuzzy KMeans clustering algorithm for highly clustered complex data and the predictive capability of VGG13 deep convolutional neural network model.The model is applied to the diagnosis of psoriasis vulgaris,and the experimental results indicate that compared with VGG13 and resNet18,the proposed approach based on deep learning and improved fuzzy KMeans is more suitable for identifying psoriasis features.
RESUMEN
Helicobacter pylori is a Gram-negative, microaerophilic bacterium that inhibits various areas of the stomach and duodenum. It causes a chronic low-level inflammation of the stomach lining and is strongly linked to the development of duodenal and gastric ulcers and stomach cancer. To better understand adaptive mechanisms utilized by H.pylori within the context of the host environment, spotted-DNA microarrays was utilized to characterize in a temporal manner, the global changes in gene expression in response to low pH in the pathogenic H. pylori strain G27. Raw data of this microarray work was available in Stanford Microarray Database. Co-regulated genes may share similar expression profiles, may be involved in related functions or regulated by common regulatory elements. There are different approaches to analyse the large-scale gene expression data in which the essence is to identify gene clusters. This approach has allowed us to (i) determine expression profiles of previously described developmentally regulated genes, (ii) identify novel developmentally regulated genes. The Helicobacter pylori is an important human and veterinary pathogen. In this work raw data of Helicobacter pylori is used as a sample to find out the coexpressed gene.