Title: BMSD-CDE: a robust community detection ensemble method for biomarker identification

Authors: Bikash Baruah; Manash P. Dutta; Subhasish Banerjee; Dhruba K. Bhattacharyya

Addresses: Department of Computer Science and Engineering, NIT Arunachal Pradesh, India; School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India ' Department of Computer Science and Information Technology, Cotton University, Guwahati, Assam, India ' Department of Computer Science and Engineering, NIT Arunachal Pradesh, India ' Department of Computer Science and Engineering, School of Engineering, Tezpur University, Tezpur, Assam, India

Abstract: Community detection algorithms (CDAs) are crucial for identifying cohesive groups within complex networks. However, individual CDAs often fall short of accurately uncovering all hidden communities due to their inherent biases and limitations. These algorithms are typically designed with specific objectives, which may inadvertently lead to the oversight of certain community types, resulting in partial or imprecise outcomes. To address these limitations, we propose BMSD-community detection ensemble (CDE), a novel ensemble method that integrates six prominent CDAs - FastGreedy, Infomap, LabelProp, LeadingEigen, Louvain, and Walktrap. By strategically combining the outputs of these diverse algorithms using p-value references and elite genes, BMSD-CDE enhances the accuracy and robustness of community detection. This ensemble approach provides a more reliable foundation for downstream analyses, particularly in identifying potential biomarkers. Applied to esophageal squamous cell carcinoma (ESCC), BMSD-CDE reveals a set of genes - F2RL3, ATP6V1C2, CGN, CAD, ANGPT2, ALDH2, CLDN7, and DTX2- as potential biomarkers. These findings are supported by extensive topological and biological analyses across normal and disease conditions using four distinct datasets.

Keywords: potential biomarker; community detection algorithm; CDA; ensemble algorithm; topological experiment; ESCC; biological validation; community detection ensemble; CDE.

DOI: 10.1504/IJDMB.2026.154691

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.3/4, pp.234 - 264

Received: 31 Oct 2023
Accepted: 03 Sep 2024
Published online: 10 Jul 2026 *

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