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.
|Title of host publication||2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020)|
|Place of Publication||Seoul, Korea (South)|
|Number of pages||6|
|Publication status||Published (in print/issue) - 13 Jan 2021|
|Event||2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Seoul, Korea, Republic of|
Duration: 16 Dec 2020 → 19 Dec 2020
|Name||Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020|
|Conference||2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)|
|Country/Territory||Korea, Republic of|
|Period||16/12/20 → 19/12/20|
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China (61972268, 61572332) and the Key Research Project of Sichuan Science and Technology Program (2020YFG0034, 2020YFS0574).
© 2020 IEEE.
- similar disease detection
- disease information network