SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction

doi:10.1109/TCBBIO.2025.3592820

CSpace

	SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction
	Ma, Ziping 1; Min, Weiqing 2; Zhang, Huanpu 3; Huang, Yulei 4; Jiang, Shuqiang 5
	2025-11-01
发表期刊	IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷号	22 期号:6 页码:2477-2490
摘要	Protein-protein interactions (PPIs) play an indispensable role in understanding disease-causing mechanisms, and the basic laws of food and drugs on life. Contemporary research on this issue, however, is incapable of guaranteeing structure consistency between extracted features and raw data, and fails to fully investigate the interconnection information of features. Thus, this paper proposes a subspace structure consistency-based method for protein-protein interactions prediction. SSC-PPI is not only capable of investigating the coherent relations between the encoded features generated from amino acid composition and conjoint triad numeric composition of F-vector, composition and transition descriptors, but also fully maintains the latent geometrical structure consistency between feature subspace and data space. Numerous comparative experiments demonstrate its excellent predictable performance with significant accuracies of 100%, 99.95%, 99.98%, 100% and 100% respectively on Helicobacter pylori, Human, Saccharomyces cerevisiae (core subset), Human-Bacillus Anthracis and Human-Yersinia pestis datasets, significantly outperforming the comparative models by average increases of 14.39%, 5.45%, 8.10%, 6.05% and 8.79% respectively. Additionally, SSC-PPI offers an efficient and reliable framework for large-scale prediction tasks such as drug-drug and drug-food interactions.
关键词	Proteins Feature extraction Drugs Diseases Amino acids Accuracy Predictive models Dimensionality reduction Data mining Databases Protein-protein interactions dimension reduction unsupervised feature selection latent representation learning
DOI	10.1109/TCBBIO.2025.3592820
收录类别	SCI
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:001635805600012
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42948
专题	中国科学院计算技术研究所
通讯作者	Ma, Ziping
作者单位	1.North Minzu Univ, Sch Math & Informat Sci, Yinchuan 750021, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.North Minzu Univ, Sch Comp Sci & Engn, Yinchuan 750021, Peoples R China 4.Ningxia Univ, Sch Math & Stat, Yinchuan 750030, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ma, Ziping,Min, Weiqing,Zhang, Huanpu,et al. SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction[J]. IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2025,22(6):2477-2490.
APA	Ma, Ziping,Min, Weiqing,Zhang, Huanpu,Huang, Yulei,&Jiang, Shuqiang.(2025).SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction.IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,22(6),2477-2490.
MLA	Ma, Ziping,et al."SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction".IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 22.6(2025):2477-2490.