ArticleBMC bioinformatics2019
Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 14 citations in OpenAlex.
- ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.BMC genomics · 2024Article
- An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks.Frontiers in genetics · 2021Article
- DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network.Frontiers in genetics · 2020Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.
methodsIn this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores.
resultsThe experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes.
conclusionsOur proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.
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