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BMC Genomics. 2017 Jan 25;18(Suppl 1):1043. doi: 10.1186/s12864-016-3263-4.

Predicting disease-related genes using integrated biomedical networks.

Author information

1
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
2
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
3
Current address: Tencent, Inc., Shenzhen, China.
4
Department of Mathematics, Harbin Institute of Technology, Harbin, China.
5
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
6
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. ydwang@hit.edu.cn.
7
Institue of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, 40536, KY, USA. jinchen@msu.edu.
8
Department of Energy Plant Research Lab, Michigan State University, East Lansing, 48824, MI, USA. jinchen@msu.edu.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

The experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.

KEYWORDS:

Disease gene prediction; Integrated network; Laplacian normalization; Supervised random walk

PMID:
28198675
PMCID:
PMC5310285
DOI:
10.1186/s12864-016-3263-4
[Indexed for MEDLINE]
Free PMC Article

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