Abstract
Proteins complexes accomplish biological functions such as transcription of DNA and translation of mRNA. Detecting protein complexes correctly and efficiently is becoming a challenging task. This paper presents a novel algorithm, core-attachment based on ant colony optimization (CA-ACO), which detects complexes in three stages. Firstly, initialize the similarity matrix. Secondly, complexes are predicted by clustering in the dynamic PPI networks. In the step, the clustering coefficient of every node is also computed. A node whose clustering coefficient is greater than the threshold is added to the core protein set. Then we mark every neighbor node of core proteins with unique core label during picking and dropping. Thirdly, filtering processes are carried out to obtain the final complex set. Experimental results show that CA-ACO algorithm had great superiority in precision, recall and f-measure compared with the state-of-the-art methods such as ClusterONE, DPClus, MCODE and so on.
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Liang, J., Lei, X., Guo, L., Tan, Y. (2018). ACO Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_11
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