Protein complex prediction in large protein–protein interaction network
Due to high computational complexity, the detection of protein complexes in large protein–protein interaction (PPI) networks remains a challenging problem. Finding the actual protein complexes from a large PPI network requires a sophisticated algorithm. The protein complexes exhibit in densely conne...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914822000934 |
| Summary: | Due to high computational complexity, the detection of protein complexes in large protein–protein interaction (PPI) networks remains a challenging problem. Finding the actual protein complexes from a large PPI network requires a sophisticated algorithm. The protein complexes exhibit in densely connected sub-graphs in a PPI network. This paper presents a novel algorithm based on a metaheuristic method for protein complex prediction in large PPI networks. The algorithm mimics the density-based graph clustering method with biological heuristics to identify the protein complexes. The algorithm is enhanced by a local search algorithm and three repair operators. A new function has been developed for computing cluster density. The method was applied to the yeast and human protein interaction data and compared with the state-of-the-art algorithms. The comparisons demonstrate the best performance of the proposed algorithm in terms of accuracy and f-measure. |
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| ISSN: | 2352-9148 |