Comparing methods for module identification in grammatical evolution

Swafford, John Mark; Nicolau, Miguel; Hemberg, Erik; O’Neill, Michael; Brabazon, Anthony

doi:10.1145/2330163.2330277

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Recently, many MI algorithms have been proposed for disease module identification [56], [57], [58], [59], [60], [61], [62], [63], [64]. According to local hypothesis, all cellular components in the same topological module are very likely to have the same molecular function and thus to be involved in the same disease [55].…”

Section: Identificationmentioning

confidence: 99%

Cooperative Co-evolutionary Module Identification with Application to Cancer Disease Module Discovery

Jia

Zhu

et al. 2016

IEEE Trans. Evol. Computat.

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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. 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, the scalability and resolution limit problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called Cooperative Co-evolutionary Module Identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the resolution limit problem. The performance of our algorithm is evaluated on twelve 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 the progression of glioma. 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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Section: Identificationmentioning

confidence: 99%

Cooperative Co-evolutionary Module Identification with Application to Cancer Disease Module Discovery

Jia

Zhu

et al. 2016

IEEE Trans. Evol. Computat.

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show abstract

“…Researchers have explored different approaches to modularity in GE, ranging from static grammar-defined functions (a variation on ADFs) [469] dynamically defined variants using dynamic grammars (i.e., grammars which automatically update to incorporate new ADFs) [255] and metagrammardefined ADFs [263]. More recently, Swafford et al have examined a number of different approaches to identify and then incorporate subderivation trees as modules [608,609,610,611,612,607]. In all of the above approaches, modularity is found to provide performance gains on problems which have sufficient 'difficulty' to warrant the overhead of increasing the search space by including mechanisms for modularity.…”

Section: Modularitymentioning

confidence: 99%