A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks

Zhan, Youlin1,2,3; Liu, Jiahan1,2,3; Wu, Min4; Tan, Chris Soon Heng5; Li, Xiaoli4; Ou-Yang, Le1,2,3,6

2023-06-01

10.1016/j.compbiomed.2023.106936

COMPUTERS IN BIOLOGY AND MEDICINE   影响因子和分区

0010-4825

1879-0534

Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein-protein interaction (PPI) data enables the identification of protein complexes computationally. Although a great number of computational methods have been proposed to identify protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply co-complex relationships, taking into account the signs of PPIs can benefit the identification of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing methods are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering (PS-SNC) model for identifying protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that our PS-SNC model can achieve better performance than other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.

Protein-protein interaction Protein complex Signed network Multi-network clustering

SCI ; EI

英语

其他

National Natural Science Foundation of China["62173235","22074060","22150610470"] ; Guangdong Basic and Applied Basic Research Foundation[2022A1515010146] ; Shenzhen Science and Technology Program[RCYX20221008092922051] ; (Key) Project of Department of Education of Guangdong Province[2022ZDZX1022]

Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology

Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology

WOS:000989050300001

PERGAMON-ELSEVIER SCIENCE LTD

20231714005894

Cell culture ; Cobalt compounds ; Complex networks ; Complexation

Computer Systems and Equipment:722 ; Chemical Reactions:802.2 ; Organic Compounds:804.1

COMPUTER SCIENCE

Web of Science

1.Shenzhen Univ, Coll Elect & Informat Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China
2.Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen Key Lab Media Secur, Shenzhen 518060, Peoples R China
3.Shenzhen Univ, Coll Elect & Informat Engn, Guangdong Lab Artificial Intelligence & Digital Ec, Shenzhen 518060, Peoples R China
4.ASTAR, Inst Infocomm Res I2R, Singapore 138632, Singapore
5.Southern Univ Sci & Technol, Coll Sci, Dept Chem, Shenzhen 518055, Peoples R China
6.Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen 518129, Peoples R China

Zhan, Youlin,Liu, Jiahan,Wu, Min,et al. A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2023,159.

Zhan, Youlin,Liu, Jiahan,Wu, Min,Tan, Chris Soon Heng,Li, Xiaoli,&Ou-Yang, Le.(2023).A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks.COMPUTERS IN BIOLOGY AND MEDICINE,159.

Zhan, Youlin,et al."A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks".COMPUTERS IN BIOLOGY AND MEDICINE 159(2023).