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	Method for identifying the active module in biological graphs with multi-component vertex weights</div>

Dmitrii A. Usoltsev, Molotkov Ivan I., Artomov Mykyta N., Sergushichev Alexey A. , Shalyto Anatoly A.

2025 , VOLUME 25, NUMBER 3 ( may-june )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2025-25-3-487-497

Method for identifying the active module in biological graphs with multi-component vertex weights

D. A. Usoltsev, I. I. Molotkov, M. N. Artomov, A. A. Sergushichev, A. A. Shalyto

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Article in Russian

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Usoltsev D.A., Molotkov I.I., Artomov M.N., Sergushichev A.A., Shalyto A.A. Method for identifying the active module in biological graphs with multi-component vertex weights. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2025, vol. 25, no. 3, pp. 487–497 (in Russian). doi: 10.17586/2226-1494-2025-25-3-487-497

Abstract

An active module in biological graphs is a connected subgraph whose vertices share a common biological function. To identify an active module, one must first construct a weighted biological graph. The weight of each vertex is calculated based on biological experiments investigating the target biological function. However, the results of a single experiment may not fully describe the desired active module, covering only part of it and potentially introducing uncertainty into the vertex weights. This work demonstrates that employing Fisher’s method to integrate data from multiple experiments followed by applying a Markov chain Monte Carlo (MCMC) and machine learning–based approach to the results of Fisher’s method, enables more effective identification of active modules in biological graphs. The study utilizes the InWebIM protein–protein interaction graph, a human brain reconstruction graph from the BigBrain project, and a gene graph for the organism Caenorhabditis elegans. To combine the results of several experiments into a single outcome within one graph, Fisher’s method is applied. Afterwards, the search for active modules is conducted using an MCMC and machine learning-based method. To validate the proposed method on real data, results from Genome- Wide Association Studies on schizophrenia and smoking are used, along with the gene expression matrix of patients with skin melanoma from the TCGA project. Applying Fisher’s method makes it possible to consider the results of multiple biological experiments simultaneously. Subsequent use of the MCMC and machine learning–based method improves the accuracy of identifying active modules compared to ranking graph vertices solely by Fisher’s method. Considering the results of multiple biological experiments when determining active modules plays a crucial role in increasing the accuracy of identifying the vertices of the active module. This, in turn, promotes a deeper understanding of the biological mechanisms of diseases, which can be of great significance for the development of new diagnostic and therapeutic methods.

Keywords: graphs, MCMC, Fisher’s method, biological graphs, active module

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