A New Method for Module Identification with Application to Cancer Disease Module Discovery

What is it about?

Module identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. This paper proposes a novel module identification algorithm called cooperative co-evolutionary module identification. The proposed algorithm employs a cooperative co-evolutionary framework to handle large-scale networks. We also incorporate a recursive partitioning scheme to address the resolution limit problem effectively. The performance of our algorithm is evaluated on 12 benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low- and high-grade glioma tumours to gain insights into the molecular mechanisms that underpin glioma progression. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms.

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Photo by CDC on Unsplash

Photo by CDC on Unsplash

Why is it important?

In recent years, module identification algorithms based on stochastic optimization algorithms such as evolutionary algorithms have been demonstrated to be superior to other algorithms on small- to medium-scale networks. However, these module identification algorithms' scalability and resolution limit problems have not been fully addressed, impeding their application to real-world networks. We propose a novel module identification algorithm called cooperative co-evolutionary module identification to address these two problems.

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