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SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction
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DOI：10.1109/TCBBIO.2025.3592820.png)
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Abstract
En 中文
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.
Keywords:
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
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