Predicting disease-related genes using integrated biomedical networks

Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance i...

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Bibliographic Details
Published in:BMC genomics 2017-01, Vol.18 (Suppl 1), p.1043-1043, Article 1043
Main Authors: Peng, Jiajie, Bai, Kun, Shang, Xuequn, Wang, Guohua, Xue, Hansheng, Jin, Shuilin, Cheng, Liang, Wang, Yadong, Chen, Jin
Format: Article
Language:English
Subjects:
Algorithms
Biomedical data
Biotechnology
Computational Biology - methods
Computer applications
Computer networks
Databases, Genetic
Disease
Disease susceptibility
Diseases
Drug development
Gene Ontology
Gene Regulatory Networks
Genes
Genetic aspects
Genetic Association Studies
Genetic Predisposition to Disease
Genomics
Genotypes
Health aspects
Humans
Innovations
Methods
Molecular diagnostic techniques
Ontology
Performance enhancement
Predictions
Proteins
Random walk
Reproducibility of Results
ROC Curve
Workflow
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Summary:Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery. We propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery. The experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.
ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-016-3263-4