DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data

Abstract

Endocrinology is the study focusing on hormones and their actions. Hormones are known as chemical messengers, released into the blood, that exert functions through receptors to make an influence in the target cell. The capacity of the mammalian organism to perform as a whole unit is made possible based on two principal control mechanisms, the nervous system and the endocrine system. The endocrine system is essential in regulating growth and development, tissue function, metabolism, and reproductive processes. Endocrine diseases such as diabetes mellitus, Grave’s disease, polycystic ovary syndrome, and insulin-like growth factor I deficiency (IGFI deficiency) are classical endocrine diseases. Endocrine dysfunction is also an increasing factor of morbidity in cancer and other dangerous diseases in humans. Thus, it is essential to understand the diseases from their genetic level in order to recognize more pathogenic genes and make a great effort in understanding the pathologies of endocrine diseases. In this study, we proposed a deep learning method named DeepGP based on graph convolutional network and convolutional neural network for prioritizing susceptible genes of five endocrine diseases. To test the performance of our method, we performed 10-cross-validations on an integrated reported dataset; DeepGP obtained a performance of the area under the curve of ∼83% and area under the precision-recall curve of ∼65%. We found that type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) share most of their associated genes; therefore, we should pay more attention to the rest of the genes related to T1DM and T2DM, respectively, which could help in understanding the pathogenesis and pathologies of these diseases.