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.

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Published in BMC Genomics, v. 18, suppl 1, 1043, p. 1-11.

© The Author(s) 2017

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Funding Information

This project has been funded by the National Natural Science Foundation of China (Grant No. 61332014, 61272121); the Start Up Funding of the Northwestern Polytechnical University (Grant No. G2016KY0301); the Fundamental Research Funds for the Central Universities (Grant No. 3102016QD003); the National High Technology Research and Development Program of China grant (no. 2015AA020101, 2015AA020108, 2014AA021505).

The publication costs for this article were funded by Northwestern Polytechnical University.

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The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

12864_2016_3263_MOESM1_ESM.pdf (54 kB)
Additional file 1: Process of mapping different types of IDs.

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Additional file 2: Initial weight for difference evidence code.

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Additional file 3: Relation between parameter b and loss value.

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Additional file 4: Diseases selected as the evaluation set.