Heterogeneous network embedding enabling accurate disease association predictions
Open Access
- 23 December 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Medical Genomics
- Vol. 12 (S10), 1-17
- https://doi.org/10.1186/s12920-019-0623-3
Abstract
It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.Keywords
This publication has 31 references indexed in Scilit:
- Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network AnalysesPLOS ONE, 2013
- Similarity-based methods for potential human microRNA-disease association predictionBMC Medical Genomics, 2013
- Prediction of Associations between OMIM Diseases and MicroRNAs by Random Walk on OMIM Disease Similarity NetworkThe Scientific World Journal, 2013
- Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseasesBioinformatics, 2010
- PhenomiR: a knowledgebase for microRNA expression in diseases and biological processesGenome Biology, 2010
- miR2Disease: a manually curated database for microRNA deregulation in human diseaseNucleic Acids Research, 2009
- Human Protein Reference Database--2009 updateNucleic Acids Research, 2008
- Drug Target Identification Using Side-Effect SimilarityScience, 2008
- Network‐based global inference of human disease genesMolecular Systems Biology, 2008
- A text-mining analysis of the human phenomeEuropean Journal of Human Genetics, 2006