MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network

Wuli Xu, Lei Duan, Huiru Zheng, Jesse Li-Ling, Weipeng Jiang, Menglin Huang, Yidan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To detect similar diseases is meaningful for revealing pathogenesis, and predicting therapeutic drugs. Previous methods measure disease similarity almost according to the semantic on biomedical ontology or the function of disease-causing molecules. However, such methods mostly describe diseases from single information, which may lead to a biased description of the relationships among diseases. In this paper, we propose a novel approach, called MISSION, for measuring the disease similarity based on multimodal-information. MISSION enhances similar disease detection based on disease information network from three aspects, including disease ontology, attribute, and literature, therefore providing a comprehensive evaluation for disease similarity. Through experiments on real-world datasets, we demonstrate that MISSION is effective, efficient, and potentially useful. Further analysis shows that MISSION has the ability to detect similar diseases with varying degrees of rich information.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020)
Place of PublicationSeoul, Korea (South)
PublisherIEEE
Pages369-374
Number of pages5
ISBN (Electronic)978-1-7281-6215-7
ISBN (Print)978-1-7281-6216-4
DOIs
Publication statusPublished - 13 Jan 2021
Event2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Seoul, Korea, Republic of
Duration: 16 Dec 202019 Dec 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Abbreviated titleBIBM2020
CountryKorea, Republic of
CitySeoul
Period16/12/2019/12/20

Keywords

  • similar disease detection
  • disease information network
  • multimodal-information

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