Article: Predictive protein module based on PPI network and double clustering algorithm Journal: International Journal of Nanotechnology (IJNT) 2024 Vol.21 No.1/2 pp.112 - 128 Abstract: Protein-protein interaction (PPI) is a kind of biomolecular network which plays an important role in biological activities. In order to improve the accuracy of protein function module prediction, obtain protein function module and run timely, this paper proposes predictive protein module based on PPI network and double clustering algorithm (ISCC), which considers the characteristics of PPI network and considers nodes as two-dimensional data points. First of all, improved density-based spatial clustering of applications with noise (IDBSCAN) determines the central cluster, and then uses spectral clustering (SC) to redivide the weight; secondly, CFSFDP and Chameleon algorithm are used to filter the similarity of the central cluster, and use support vector machine (SVM) to get the final clustering result. Finally, the experiments are compared with CDUN, EA, MCL and MCODE in terms of accuracy, sensitivity, F value and the number of protein functional modules. The experimental results show that the F value of ISCCD is 70% higher than that of EA, the number of recognition modules is 257 higher than that of CDUN, and the running time is 494s faster than that of MCODE. Inderscience Publishers - linking academia, business and industry through research

Title: Predictive protein module based on PPI network and double clustering algorithm

Authors: Sicong Huo; Quansheng Liu; Tao Lu

Addresses: College of information engineering, Nanning University, Nanning, Guangxi, 530200, China ' College of information engineering, Nanning University, Nanning, Guangxi, 530200, China ' College of information engineering, Nanning University, Nanning, Guangxi, 530200, China

Abstract: Protein-protein interaction (PPI) is a kind of biomolecular network which plays an important role in biological activities. In order to improve the accuracy of protein function module prediction, obtain protein function module and run timely, this paper proposes predictive protein module based on PPI network and double clustering algorithm (ISCC), which considers the characteristics of PPI network and considers nodes as two-dimensional data points. First of all, improved density-based spatial clustering of applications with noise (IDBSCAN) determines the central cluster, and then uses spectral clustering (SC) to redivide the weight; secondly, CFSFDP and Chameleon algorithm are used to filter the similarity of the central cluster, and use support vector machine (SVM) to get the final clustering result. Finally, the experiments are compared with CDUN, EA, MCL and MCODE in terms of accuracy, sensitivity, F value and the number of protein functional modules. The experimental results show that the F value of ISCCD is 70% higher than that of EA, the number of recognition modules is 257 higher than that of CDUN, and the running time is 494s faster than that of MCODE.

Keywords: PPI network; protein function module prediction; clustering; SVM; support vector machine; IDBSCAN; improved density-based spatial clustering of applications with noise.

DOI: 10.1504/IJNT.2024.136485

International Journal of Nanotechnology, 2024 Vol.21 No.1/2, pp.112 - 128

Received: 10 Mar 2021
Accepted: 30 Jun 2021
Published online: 05 Feb 2024 *

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Title: Predictive protein module based on PPI network and double clustering algorithm

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