He, S;
Jia, G;
Zhu, Z;
Tennant, DA;
Huang, Q;
Tang, K;
Liu, J;
... Yao, X; + view all
(2016)
Cooperative Co-Evolutionary Module Identification With Application to Cancer Disease Module Discovery.
IEEE Transactions on Evolutionary Computation
, 20
(6)
pp. 874-891.
10.1109/TEVC.2016.2530311.
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Abstract
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 (RL) 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 RL problem. 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 tumors 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.
| Type: | Article |
|---|---|
| Title: | Cooperative Co-Evolutionary Module Identification With Application to Cancer Disease Module Discovery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TEVC.2016.2530311 |
| Publisher version: | http://dx.doi.org/10.1109/TEVC.2016.2530311 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Computer Science, Community detection, complex networks, cooperation co-evolutionary, module identification, PROTEIN-INTERACTION NETWORKS, DIFFERENTIAL EVOLUTION, COMMUNITY STRUCTURE, COMPLEX NETWORKS, BREAST-CANCER, GENE ONTOLOGY, OPTIMIZATION, ALGORITHM, COEVOLUTION, MODULARITY |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10051301 |
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