Protein structural domain-disease association prediction based on heterogeneous networks

Background Domains can be viewed as portable units of protein structure, folding, function, evolution, and design. Small proteins are often found to be composed of only a single domain, while most large proteins consist of multiple domains for achieving various composite cellular functions. A dysfun...

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Published in:BMC genomics Vol. 23; no. Suppl 6; pp. 869 - 15
Main Authors: Zhang, Jingpu, Deng, Lianping, Deng, Lei
Format: Journal Article
Language:English
Published: London BioMed Central 10.04.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Animal Genetics and Genomics
Biomedical and Life Sciences
China
Commuting
Computational Biology - methods
Data processing
Datasets
Disease
Disease - genetics
Diseases
Domain-disease association prediction
Genes
Health aspects
Heterogeneous networks
Humans
Life Sciences
Machine Learning
Medical research
Medicine, Experimental
Meta-path topological feature
Microarrays
Microbial Genetics and Genomics
Physiological aspects
Plant Genetics and Genomics
Protein Domains
Protein folding
Protein structure
Proteins
Proteins - chemistry
Proteins - metabolism
Proteomics
Semantics
Structure-function relationships
Topology
China
Domain-disease association prediction
Meta-path topological feature
Heterogeneous networks
ISSN:1471-2164, 1471-2164
Online Access:Get full text
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Summary:Background Domains can be viewed as portable units of protein structure, folding, function, evolution, and design. Small proteins are often found to be composed of only a single domain, while most large proteins consist of multiple domains for achieving various composite cellular functions. A dysfunction in domains may affect the function of proteins in some disease. Inferring the disease-related domains will help our understanding of the mechanism of human complex diseases. Results In this study, we firstly build a global heterogeneous information network based on structural-based domains, proteins, and diseases. Then the topological features of the network are extracted according to the meta-paths between domain and disease nodes. Finally, we train a binary classifier based on the XGBOOST (eXtreme Gradient Boosting) algorithm to predict the potential associations between domains and diseases. The results show that the binary classification model using the XGBOOST algorithm performs significantly better than models using other machine learning algorithms, achieving an AUC (Area Under Curve) score of 0.94 in the leave-one-out cross-validation experiment. Conclusions We develop a method to build a binary classifier using the topological features based on meta-paths and predict the potential associations between domains and diseases. Based on its predictive performance in independent test sets, the method is proved to be powerful. Moreover, representing domains and diseases through integrating more multi-omic data will further optimize predictive performance.
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ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-024-11117-0