Predicting disease-related genes using integrated biomedical networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-01

AUTHORS

Jiajie Peng, Kun Bai, Xuequn Shang, Guohua Wang, Hansheng Xue, Shuilin Jin, Liang Cheng, Yadong Wang, Jin Chen

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. More... »

PAGES

1043

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12864-016-3263-4

    DOI

    http://dx.doi.org/10.1186/s12864-016-3263-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1074236735

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28198675


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