Network clustering algorithm for dynamic protein complex detection fused graph embedding

Jie Wang; Ying Jia; Xiancan Yang

doi:10.1117/12.3029061

22 May 2024 Network clustering algorithm for dynamic protein complex detection fused graph embedding

Jie Wang, Ying Jia, Xiancan Yang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760T (2024) https://doi.org/10.1117/12.3029061
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Network clustering for protein complex identification within protein interaction networks has long been a focal point for researchers in the field of machine learning and data mining. Although existing algorithms take into account the dynamic properties of protein complexes, they ignore the computational dynamics. This paper introduces a novel network clustering algorithm designed for dynamic protein complex detection. This approach integrates gene expression data and static network data to construct a dynamic network construction model and incorporates multiple data sources to formulate a node representation model. It devises a new dynamic clustering model that simulates the splitting, merging, and growth of protein complexes, providing a framework for dynamic network clustering. Experimental results demonstrate that the proposed algorithm outperforms dynamic clustering algorithms and traditional clustering algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Wang, Ying Jia, and Xiancan Yang "Network clustering algorithm for dynamic protein complex detection fused graph embedding", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760T (22 May 2024); https://doi.org/10.1117/12.3029061

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