Ensemble disease gene prediction by clinical sample-based networks

Autor: Qianghua Xiao, Li-Ping Tian, Fang-Xiang Wu, Ping Luo, Bolin Chen
Rok vydání: 2020
Předmět:
Disease gene prediction
Computer science
Sample (statistics)
Breast Neoplasms
Computational biology
Disease
lcsh:Computer applications to medicine. Medical informatics
Biochemistry
Cross-validation
03 medical and health sciences
0302 clinical medicine
Structural Biology
Alzheimer Disease
Ensemble learning
Cluster Analysis
Humans
Protein Interaction Maps
Thyroid Neoplasms
Network centrality
Cluster analysis
Molecular Biology
Gene
lcsh:QH301-705.5
Sample-based networks
030304 developmental biology
Disease gene
0303 health sciences
Protein-protein interaction network
Applied Mathematics
Research
Proteins
Models
Theoretical
Computer Science Applications
Logistic Models
lcsh:Biology (General)
ROC Curve
030220 oncology & carcinogenesis
Area Under Curve
lcsh:R858-859.7
Female
DNA microarray
Centrality
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 21, Iss S2, Pp 1-12 (2020)
ISSN: 1471-2105
Popis: Background Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different. Results To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer’s disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes. Conclusions In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.
Databáze: OpenAIRE
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