Heterogeneous network embedding enabling accurate disease association predictions

• Published in: BMC medical genomics Vol. 12; no. Suppl 10; pp. 186 - 17
• Main Authors: Xiong, Yun, Guo, Mengjie, Ruan, Lu, Kong, Xiangnan, Tang, Chunlei, Zhu, Yangyong, Wang, Wei
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.12.2019; BioMed Central Ltd; BMC
• Subjects: Analysis; Biomedical and Life Sciences; Biomedicine; Computational Biology - methods; Datasets; Disease; Disease association prediction; Embedding; Gene Expression; Gene mapping; Genes; Genomics; Heterogeneous network; Human Genetics; Humans; Machine learning; Medical research; Metabolomics; Metagenomics; Microarrays; MicroRNA; miRNA; Network embedding; Neural networks; Novels; Prostate cancer; Proteins; Proteomics; Semantics; Social networks; Heterogeneous network; Disease association prediction; Network embedding
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-019-0623-3

Background 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. Results 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. Conclusions 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.