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A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm

Author

Listed:
  • Caixia Wang

    (School of International Economics, China Foreign Affairs University, 24 Zhanlan Road, Xicheng District, Beijing 100037, China)

  • Rongquan Wang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China)

  • Kaiying Jiang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China)

Abstract

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.

Suggested Citation

  • Caixia Wang & Rongquan Wang & Kaiying Jiang, 2025. "A Method for Detecting Overlapping Protein Complexes Based on an Adaptive Improved FCM Clustering Algorithm," Mathematics, MDPI, vol. 13(2), pages 1-27, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:196-:d:1563175
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Roger Guimerà & Luís A. Nunes Amaral, 2005. "Functional cartography of complex metabolic networks," Nature, Nature, vol. 433(7028), pages 895-900, February.
    3. Christian von Mering & Roland Krause & Berend Snel & Michael Cornell & Stephen G. Oliver & Stanley Fields & Peer Bork, 2002. "Comparative assessment of large-scale data sets of protein–protein interactions," Nature, Nature, vol. 417(6887), pages 399-403, May.
    4. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    5. Yuliang Pan & Diwei Liu & Lei Deng, 2017. "Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-20, June.
    6. Nevan J. Krogan & Gerard Cagney & Haiyuan Yu & Gouqing Zhong & Xinghua Guo & Alexandr Ignatchenko & Joyce Li & Shuye Pu & Nira Datta & Aaron P. Tikuisis & Thanuja Punna & José M. Peregrín-Alvarez & Mi, 2006. "Global landscape of protein complexes in the yeast Saccharomyces cerevisiae," Nature, Nature, vol. 440(7084), pages 637-643, March.
    7. Anne-Claude Gavin & Patrick Aloy & Paola Grandi & Roland Krause & Markus Boesche & Martina Marzioch & Christina Rau & Lars Juhl Jensen & Sonja Bastuck & Birgit Dümpelfeld & Angela Edelmann & Marie-Ann, 2006. "Proteome survey reveals modularity of the yeast cell machinery," Nature, Nature, vol. 440(7084), pages 631-636, March.
    8. David Eisenberg & Edward M. Marcotte & Ioannis Xenarios & Todd O. Yeates, 2000. "Protein function in the post-genomic era," Nature, Nature, vol. 405(6788), pages 823-826, June.
    9. Zhang, Shihua & Wang, Rui-Sheng & Zhang, Xiang-Sun, 2007. "Identification of overlapping community structure in complex networks using fuzzy c-means clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 483-490.
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