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KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations by Changzhi Jiang; Minli Tang; Shuting Jin; Wei Huang; Xiangrong Liu is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations about?

Accumulated studies discovered that various microbes in human bodies were closely related to complex human diseases and could provide new insight into drug development. Multiple computational methods were constructed to predict microbes that were potentially associated with diseases. However, most previous methods were based on single characteristics of microbes or diseases, that lacked important biological information related to microorganisms or diseases. Therefore, we constructed a knowledge graph centered on microorganisms and diseases from several existed databases to provide knowledgeable information for microbes and diseases. Then, we adopted a graph neural network method to learn representations of microbes and diseases from the constructed knowledge graph. After that, we introduced the Gaussian kernel similarity features of microbes and diseases to generate final representations of microbes and diseases. At last, we proposed a score function on final representations of microbes and diseases to predict scores of microbe-disease associations. Comprehensive experiments on the Human Microbe-Disease Association Database (HMDAD) dataset had demonstrated that our approach outperf

Who reads KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations?

Author

Changzhi Jiang; Minli Tang; Shuting Jin; Wei Huang; Xiangrong Liu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)