A deep learning model for identification of diabetes type 2 based on nucleotide signals.

In Genome-Wide Association Studies (GWAS), detection of T2D-related variants in genome sequences and accurate modeling of the complex structure of the relevant gene are of great importance for the diagnosis of diabetes. For this purpose, this paper presents a novel strong algorithm to accurately and effectively identify Type 2 Diabetes (T2D) risk variants at high-performance rates. The proposed algorithm consists of five important phases. The first stage is to collect T2D-associated DNA sequences and to digitize them by the Entropy-based technique. The second stage is to transform these digitized DNA sequences into 224 × 224 pixels size spectrum images. The third is to extract a distinctive feature set from these spectrum images using the ResNet and VGG19 architectures. The fourth is to classify the effective feature set using SVM and k-NN methods. The last stage is to evaluate the system with k-fold cross-validation. As a result of the developed algorithm, the performances of the used Convolutional Neural Network (CNN) methods, the Entropy-based technique, and the classifiers were compared in relation. As a result of the study a combination model of the proposed Entropy-based technique, ResNet and Support Vector Machine (SVM) achieved the highest accuracy rate with 99.09%. With this study, the performance of the system in the extraction of epigenetic features and prediction of T2D from spectrogram images was investigated. The results show that the system will contribute to the identification of all genes in diabetes-related tissue and studies on new drug targets. [ABSTRACT FROM AUTHOR]