A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships.

Journal: BMC medical informatics and decision making
Published Date: Jul 18, 2025

Abstract

BACKGROUND: Predicting associations between microbes and diseases is crucial for clinical diagnosis and therapy. However, biological experiments are time-intensive, necessitating efficient computational models. Traditional models rely on existing microbe-disease associations, but limited data often restricts their effectiveness. This scarcity of information hinders the construction of a comprehensive association network, prompting the need for innovative solutions.

Authors

  • Shaopeng Liu
    Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510006, China; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China.
  • Wanlu Hu
    School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.
  • Chun-Chun Wang
    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
  • Linlin Zhuo
    School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang 325035, China; College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
  • Xu Lu
    Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510006, China. Electronic address: bruda@126.com.

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