https://www.selleckchem.com/products/mavoglurant.html Convolutional neural networks (CNNs) are a branch of deep learning which have been turned into one of the popular methods in different applications, especially medical imaging. One of the significant applications in this category is to help specialists make an early detection of skin cancer in dermoscopy and can reduce mortality rate. However, there are a lot of reasons that affect system diagnosis accuracy. In recent years, the utilization of computer-aided technology for this purpose has been turned into an interesting category for scientists. In this research, a meta-heuristic optimized CNN classifier is applied for pre-trained network models for visual datasets with the purpose of classifying skin cancer images. However there are different methods about optimizing the learning step of neural networks, and there are few studies about the deep learning based neural networks and their applications. In the present work, a new approach based on whale optimization algorithm is utilized for optimizing the weight and biases in the CNN models. The new method is then compared with 10 popular classifiers on two skin cancer datasets including DermIS Digital Database Dermquest Database. Experimental results show that the use of this optimized method performs with better accuracy than other classification methods. © 2020 Long Zhang et al., published by De Gruyter.Herein, we assess the gene expression changes activated in thyroid tumors through a computational approach, using the MapReduce algorithm. Through this predictive analysis, we identified the TfR1 gene as a critical mediator of thyroid tumor progression. Then, we investigated the effect of TfR1 gene silencing through small interfering RNA (siRNA) in the expression of extracellular signal-regulated kinase 1/2 (Erk1/2) pathway and c-Myc in human differentiated follicular and undifferentiated anaplastic thyroid cancer. The expression levels of cyclin D1, p53, and p27,