An Ontology-Independent Representation Learning for Similar Disease Detection Based on Multi-layer Similarity Network

Ruiqi Qin, Lei Duan, Huiru Zheng, Jesse Li-Ling, Kaiwen Song, Yidan Zhang

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Abstract

To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively.
Original languageEnglish
Pages (from-to)183-193
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume18
Issue number1
Early online date16 Sep 2019
DOIs
Publication statusE-pub ahead of print - 16 Sep 2019

Keywords

  • disease similarity
  • disease information network
  • representation learning
  • multi-layer similarity network

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