Abstract
DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein. Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken. In this research, the authors propose a hybrid deep learning model based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify the DNA sequence for the insulin gene to predict type 2-diabetes based on gene sequence mutation. To evaluate the proposed models, we used several performance indexes such as accuracy, precision, sensitivity, recall, and F1 score. The experiments shown in the paper reveal that the proposed model accomplished the best results. The overall accuracy of the learning process is recorded as 99% for the proposed hybrid LSTM-CNN model while it is recorded as 97.5%, and 95% for CNN, and LSTM, respectively.
Highlights
Artificial intelligence, deep learning, is ushering in a new era in clinical medicine
The authors propose a hybrid deep learning model based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify the Deoxyribonucleic Acid (DNA) sequence for the insulin gene to predict type 2-diabetes based on gene sequence mutation
To assess the performance of the proposed LSTM-CNN model, experiments based on the same insulin gene against LSTM, CNN are performed
Summary
Artificial intelligence, deep learning, is ushering in a new era in clinical medicine. The main idea of the AI system is to train the health data that has been previously labeled and interpreted by human experts. This training process assists the AI system in learning and performing the interpretation process on new incoming data of the same kind. This interpretation may be to detect or predict a disease state. Machine learning (ML) and deep learning (DL) are part of artificial intelligence, extensively utilized in several genomics studies.
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