Ant Colony Optimization with Multi-Agent Evolution for Detecting Functional Modules in Protein-Protein Interaction Networks

Ji, Junzhong; Liu, Zhijun; Zhang, Aidong; Jiao, Lang; Liu, Chunnian

doi:10.1007/978-3-642-34062-8_58

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…throughput experimental technologies remain limited by their high cost and time-consuming application, developing effective and efficient computational approaches for detecting PFMs in PPINs has become an essential and challenging problem in computational biology [6]. A PPIN is a hierarchical and modular network, which can be typically represented by a connection graph, where the individual proteins and the interactions between proteins correspond to nodes and edges in the network, respectively [7].…”

Section: Plos Onementioning

confidence: 99%

Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks

Ying

Lin

2020

PLoS ONE

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Protein Function Module (PFM) identification in Protein-Protein Interaction Networks (PPINs) is one of the most important and challenging tasks in computational biology. The quick and accurate detection of PFMs in PPINs can contribute greatly to the understanding of the functions, properties, and biological mechanisms in research on various diseases and the development of new medicines. Despite the performance of existing detection approaches being improved to some extent, there are still opportunities for further enhancements in the efficiency, accuracy, and robustness of such detection methods. Based on the uniqueness of the network-clustering problem in the context of PPINs, this study proposed a very effective and efficient model based on the Lin-Kernighan-Helsgaun algorithm for detecting PFMs in PPINs. To demonstrate the effectiveness and efficiency of the proposed model, computational experiments are performed using three different categories of species datasets. The computational results reveal that the proposed model outperforms existing detection techniques in terms of two key performance indices, i.e., the degree of polymerization inside PFMs (cohesion) and the deviation degree between PFMs (separation), while being very fast and robust. The proposed model can be used to help researchers decide whether to conduct further expensive and time-consuming biological experiments and to select target proteins from large-scale PPI data for further detailed research.

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Section: Plos Onementioning

confidence: 99%

Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks

Ying

Lin

2020

PLoS ONE

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“…However, this algorithm easily falls into the local optimum. In literature [26], a new ACO-MAE mechanism that combines ACO with the idea of multi-agent evolution (MAE) was developed to achieve better prediction accuracy.…”

Section: Existing Workmentioning

confidence: 99%

Simplified Swarm Optimization Based Function Modules Detection in Protein-to-Protein Interaction Networks

Zheng¹,

Wu²,

Shao-zhen

et al. 2017

Preprint

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Abstract. Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein-protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

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“…By taking the detection of functional modules as the optimization problem of solving Traveling Salesman Problem, ACOPIN adapts Ant Colony Optimization (ACO) algorithm into PPI network problems. In 2012, Ji et al proposed a new algorithm, which combines ant colony optimization with multi-agent evolution (ACO-MAE) to explore and exploit search spaces for detecting functional modules [13]. This algorithm has both the advantage of ACO, the ability to find feasible solutions by means of collaborations among ants, and that of MAE, the ability to further extend search subspaces and then to move out of local optima.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks

Liu

Yang

et al. 2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Swarm intelligence algorithms have been successfully applied to the detection of functional modules in PPI networks. As the increasing of the PPI network size, those algorithms will cost more time in functional module detection. In this paper, we present a novel algorithm, ACC-MLF, which combines ant colony clustering with multilevel framework to reduce the runtime in the large-scale PPI networks. First, use a new matching strategy to coarsen the original large-scale PPI network, and get a smaller PPI network. Then, use the ant colony clustering algorithm to cluster the obtained network. Finally, get the clustering result of original network through de-coarsening and use the refinement to avoid the result from falling into the local optimal. Experiments in some large-scale networks show that the detecting speed of ACC-MLF has significantly improved in contrast to ACC-FMD, and ACC-MLF can get better clustering results in some evaluation metrics while compared with ACC-FMD, MCODE, MINE and Core algorithms.

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Ant Colony Optimization with Multi-Agent Evolution for Detecting Functional Modules in Protein-Protein Interaction Networks

Cited by 16 publications

References 18 publications

Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks

Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks

Simplified Swarm Optimization Based Function Modules Detection in Protein-to-Protein Interaction Networks

Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks

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