An Integrative Disease Information Network Approach to Similar Disease detection

Wuli Xu, Lei Duan, Huiru Zheng, Jesse Li-Ling, Weipeng Jiang, Yidan Zhang, Tingting Wang, Ruiqi Qin

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Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g.,
disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrate approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established firstly. And then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information
network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.
Original languageEnglish
Pages (from-to)1
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Early online date3 Sept 2021
Publication statusPublished (in print/issue) - 3 Sept 2021

Bibliographical note

Publisher Copyright:


  • similar disease detection
  • disease information network
  • multimodal-information
  • Computer science
  • Semantics
  • Taxonomy
  • Biomedical measurement
  • Ontologies
  • Feature extraction
  • Diseases


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