AUTHOR=Zhang Ningyi , Wang Haoyan , Xu Chen , Zhang Liyuan , Zang Tianyi 

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

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.700061

DOI=10.3389/fcell.2021.700061

ISSN=2296-634X

ABSTRACT=Endocrinology is the study focus on hormones and their actions. Hormones are known as chemical messengers, released into the blood, exert functions through receptors to make an influence in the target cell.  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 (PCOS), 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 of human. Thus it is essential to understand the diseases from genetic level, in order to recognizing 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 (GCN) and convolutional neural network (CNN) for prioritizing susceptible genes of five endocrine diseases. To test the performance of our method, we performed a 10-cross validations on an integrated reported dataset, DeepGP obtained a performance of the AUC of ~83% and AUPR of ~65%. We found that T1DM and T2DM share most of their associated genes, therefore we should pay more attention to the rest genes related to T1DM and T2DM, respectively, which could help in understanding the pathogenesis and pathologies of these diseases.