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.
Highlights
Endocrine diseases fall into broad categories of hormone over- or underproduction, modulate tissue response to hormones, or tumors caused by endocrine tissue (Belfiore and LeRoith, 2018)
DeepGP contains three main parts, data preprocessing, feature reconstruction based on Graph convolutional network (GCN), and endocrine disease-related gene prediction based on convolutional neural network (CNN)
We evaluated the performance of DeepGP and classic machine learning methods, such as support vector machine (SVM), random forest (RF), Naïve Bayes, and deep neural network (DNN) for predicting disease–gene associations
Summary
Endocrine diseases fall into broad categories of hormone over- or underproduction, modulate tissue response to hormones, or tumors caused by endocrine tissue (Belfiore and LeRoith, 2018). Hormones synthesized and released by the endocrine glands exert their functions by regulating the biological process of cells. There are several examples of common endocrine diseases: type I/II diabetes mellitus, Graves’ disease (GD), polycystic ovary syndrome (PCOS), and insulin-like growth factor I (IGFI) deficiency, etc. Genome-wide association studies (GWAS) have reported numerous gene regions associated with different endocrine diseases. Graves’ disease is an organ-specific autoimmune thyroid disease, resulting from excessive secretion of thyroid hormones by thyroid tissue (Dvornikova et al, 2020). The pathogenesis of GD is mediated by the production of antibodies to TSH receptors, which provide increased secretion of thyroid hormones and a rapid growth of the thyroid after stimulation (Smith et al, 2018; Soh and Aw, 2019). It has been identified that associations between CTLA-4, FOXP3, TLR class polymorphism, and a number of pathological conditions develop in GD (Xiao et al, 2015; Fathima et al, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
