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

doi:10.1109/BIBM49941.2020.9313407

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 proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020)
Place of Publication	Seoul, Korea (South)
Publisher	IEEE
Pages	369-374
Number of pages	6
ISBN (Electronic)	978-1-7281-6215-7
ISBN (Print)	978-1-7281-6216-4
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313407
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

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Abbreviated title	BIBM2020
Country/Territory	Korea, Republic of
City	Seoul
Period	16/12/20 → 19/12/20

Bibliographical note

Funding 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).

Publisher Copyright:
© 2020 IEEE.

Keywords

similar disease detection
disease information network
multimodal-information

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM49941.2020.9313407

Cite this

Xu, W., Duan, L., Zheng, H., Li-Ling, J., Jiang, W., Huang, M., & Zhang, Y. (2021). MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020) (pp. 369-374). Article 9313407 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313407

Xu, Wuli ; Duan, Lei ; Zheng, Huiru et al. / MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020). Seoul, Korea (South) : IEEE, 2021. pp. 369-374 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{1eae4e1b042a41a6bc8d9f61017bab34,

title = "MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network",

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.",

keywords = "similar disease detection, disease information network, multimodal-information",

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

note = "Funding 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). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), BIBM2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2021",

month = jan,

day = "13",

doi = "10.1109/BIBM49941.2020.9313407",

language = "English",

isbn = "978-1-7281-6216-4",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

publisher = "IEEE",

pages = "369--374",

booktitle = "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020)",

address = "United States",

}

Xu, W, Duan, L, Zheng, H, Li-Ling, J, Jiang, W, Huang, M & Zhang, Y 2021, MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network. in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020)., 9313407, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, IEEE, Seoul, Korea (South), pp. 369-374, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea, Republic of, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313407

MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network. / Xu, Wuli; Duan, Lei; Zheng, Huiru et al.
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020). Seoul, Korea (South): IEEE, 2021. p. 369-374 9313407 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network

AU - Xu, Wuli

AU - Duan, Lei

AU - Zheng, Huiru

AU - Li-Ling, Jesse

AU - Jiang, Weipeng

AU - Huang, Menglin

AU - Zhang, Yidan

N1 - Funding 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). Publisher Copyright: © 2020 IEEE.

PY - 2021/1/13

Y1 - 2021/1/13

N2 - 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.

AB - 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.

KW - similar disease detection

KW - disease information network

KW - multimodal-information

UR - http://www.scopus.com/inward/record.url?scp=85100348622&partnerID=8YFLogxK

U2 - 10.1109/BIBM49941.2020.9313407

DO - 10.1109/BIBM49941.2020.9313407

M3 - Conference contribution

SN - 978-1-7281-6216-4

T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

SP - 369

EP - 374

BT - 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020)

PB - IEEE

CY - Seoul, Korea (South)

T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Y2 - 16 December 2020 through 19 December 2020

ER -

Xu W, Duan L, Zheng H, Li-Ling J, Jiang W, Huang M et al. MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020). Seoul, Korea (South): IEEE. 2021. p. 369-374. 9313407. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313407

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this