Author(s)

Sesugh Gabriel Abenga¹, Kehinde Seyi Olalekan², Francis Akogwu Alu³, Stephen Yavenga Uyoo¹

¹Department of Computer Science, Joseph Sarwuan Tarka University, Makurdi, Nigeria.
²Department of Information Technology Department, National Orthopaedic Hospital, Lagos, Nigeria.
³Department of Information Security/Data Management, Franksong Synergy Consultancy, Upper Marlboro, USA.

Diabetic Kidney Disease (DKD) is a common chronic complication of diabetes. Despite advancements in accurately identifying biomarkers for detecting and diagnosing this harmful disease, there remains an urgent need for new biomarkers to enable early detection of DKD. In this study, we modeled publicly available transcriptome datasets as a graph problem and used GraphSAGE Neural Networks (GNNs) to identify potential biomarkers. The GraphSAGE model effectively learned representations that captured the intricate interactions, dependencies among genes, and disease-specific gene expression patterns necessary to classify samples as DKD and Control. We finally extracted the features of importance; the identified set of genes exhibited an impressive ability to distinguish between healthy and unhealthy samples, even though these genes differ from previous research findings. The unexpected biomarker variations in this study suggest more exploration and validation studies for discovering biomarkers in DKD. In conclusion, our study showcases the effectiveness of modeling transcriptome data as a graph problem, demonstrates the use of GraphSAGE models for biomarker discovery in DKD, and advocates for integrating advanced machine-learning techniques in DKD biomarker research, emphasizing the need for a holistic approach to unravel the intricacies of biological systems.

Diabetic Kidney Disease (DKD), GraphSAGE Neural Network, Personalized Treatment, Transcriptome, Gene Expression, Differential Analysis, Deep Learning, End-Stage Kidney Disease (ESKD), Early Detection

Abenga, S. , Olalekan, K. , Alu, F. and Uyoo, S. (2023) Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network. Journal of Computer and Communications, 11, 51-63. doi: 10.4236/jcc.2023.1110004.