An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks

Wang, Rongquan; Ma, Huimin; Wang, Caixia

doi:10.3389/fgene.2022.839949

Cited by 5 publications

(3 citation statements)

References 72 publications

(110 reference statements)

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“…It is considered the best clustering output ( HMs best ), and its parameters are appropriate for the input PPI network of the MP algorithm. At this time, this harmony is the identified protein complexes (IPCs)(lines [40][41][42].…”

Section: Mp-ahsa Algorithmmentioning

confidence: 97%

“…Furthermore, Liu et al [41] proposed a new algorithm based on a semi-supervised model to identify significant protein complexes with clear module structures. Additionally, ELF-DPC [42] is an ensemble learning framework for detecting protein complexes based on structural modularity and a trained voting regressor model. However, the performance of these methods is limited by the training data size.…”

Section: Data Integrationmentioning

confidence: 99%

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Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

Wang

Wang²,

Ma³

2022

BMC Bioinformatics

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Background Accurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks. Result To address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm’s parameters automatically. Conclusions Experimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.

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Section: Mp-ahsa Algorithmmentioning

confidence: 97%

Section: Data Integrationmentioning

confidence: 99%

Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

Wang

Wang²,

Ma³

2022

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Subsequently, a network clustering was performed in this transformed vector space [21,22]. One example of such an algorithm is the ensemble learning framework for density peak clustering (ELF-DPC) [23]. ELF-DPC first maps the PPI network to the vector space and constructs a weighted network to identify core edges.…”

Section: Introductionmentioning

confidence: 99%

A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic

et al. 2023

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Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance.

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Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

Sahoo¹,

Patra²,

Vipsita³

2023

Computational Biology and Chemistry

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An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks

Cited by 5 publications

References 72 publications

Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

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