Predicting disease-related genes using integrated biomedical networks

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

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


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.

Original languageEnglish
Article number1043
JournalBMC Genomics
StatePublished - Jan 25 2017

Bibliographical note

Publisher Copyright:
© 2017 The Author(s).


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

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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