A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm
A protein complex can be regarded as a functional module developed by interacting proteins. The protein complex has attracted significant attention in bioinformatics as a critical substance in life activities. Identifying protein complexes in protein–protein interaction (PPI) networks is vital in li...
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/196 |
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| author | Caixia Wang Rongquan Wang Kaiying Jiang |
| author_facet | Caixia Wang Rongquan Wang Kaiying Jiang |
| author_sort | Caixia Wang |
| collection | DOAJ |
| description | A protein complex can be regarded as a functional module developed by interacting proteins. The protein complex has attracted significant attention in bioinformatics as a critical substance in life activities. Identifying protein complexes in protein–protein interaction (PPI) networks is vital in life sciences and biological activities. Therefore, significant efforts have been made recently in biological experimental methods and computing methods to detect protein complexes accurately. This study proposed a new method for PPI networks to facilitate the processing and development of the following algorithms. Then, a combination of the improved density peaks clustering algorithm (DPC) and the fuzzy C-means clustering algorithm (FCM) was proposed to overcome the shortcomings of the traditional FCM algorithm. In other words, the rationality of results obtained using the FCM algorithm is closely related to the selection of cluster centers. The objective function of the FCM algorithm was redesigned based on ‘high cohesion’ and ‘low coupling’. An adaptive parameter-adjusting algorithm was designed to optimize the parameters of the proposed detection algorithm. This algorithm is denoted as the DFPO algorithm (DPC-FCM Parameter Optimization). Finally, the performance of the DFPO algorithm was evaluated using multiple metrics and compared with over ten state-of-the-art protein complex detection algorithms. Experimental results indicate that the proposed DFPO algorithm exhibits improved detection accuracy compared with other algorithms. |
| format | Article |
| id | doaj-art-681288c0486243948838a0dd540d48a5 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-681288c0486243948838a0dd540d48a52025-01-24T13:39:42ZengMDPI AGMathematics2227-73902025-01-0113219610.3390/math13020196A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering AlgorithmCaixia Wang0Rongquan Wang1Kaiying Jiang2School of International Economics, China Foreign Affairs University, 24 Zhanlan Road, Xicheng District, Beijing 100037, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaA protein complex can be regarded as a functional module developed by interacting proteins. The protein complex has attracted significant attention in bioinformatics as a critical substance in life activities. Identifying protein complexes in protein–protein interaction (PPI) networks is vital in life sciences and biological activities. Therefore, significant efforts have been made recently in biological experimental methods and computing methods to detect protein complexes accurately. This study proposed a new method for PPI networks to facilitate the processing and development of the following algorithms. Then, a combination of the improved density peaks clustering algorithm (DPC) and the fuzzy C-means clustering algorithm (FCM) was proposed to overcome the shortcomings of the traditional FCM algorithm. In other words, the rationality of results obtained using the FCM algorithm is closely related to the selection of cluster centers. The objective function of the FCM algorithm was redesigned based on ‘high cohesion’ and ‘low coupling’. An adaptive parameter-adjusting algorithm was designed to optimize the parameters of the proposed detection algorithm. This algorithm is denoted as the DFPO algorithm (DPC-FCM Parameter Optimization). Finally, the performance of the DFPO algorithm was evaluated using multiple metrics and compared with over ten state-of-the-art protein complex detection algorithms. Experimental results indicate that the proposed DFPO algorithm exhibits improved detection accuracy compared with other algorithms.https://www.mdpi.com/2227-7390/13/2/196protein–protein interaction networkprotein complexesfuzzy clustering algorithmdensity peaks clustering algorithmparameter optimizationswarm intelligence optimization algorithm |
| spellingShingle | Caixia Wang Rongquan Wang Kaiying Jiang A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm Mathematics protein–protein interaction network protein complexes fuzzy clustering algorithm density peaks clustering algorithm parameter optimization swarm intelligence optimization algorithm |
| title | A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm |
| title_full | A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm |
| title_fullStr | A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm |
| title_full_unstemmed | A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm |
| title_short | A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm |
| title_sort | method for detecting overlapping protein complexes based on an adaptive improved fcm clustering algorithm |
| topic | protein–protein interaction network protein complexes fuzzy clustering algorithm density peaks clustering algorithm parameter optimization swarm intelligence optimization algorithm |
| url | https://www.mdpi.com/2227-7390/13/2/196 |
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