MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System

Kai Zheng; Zhu Hong You; Lei Wang; Yi Ran Li; Yan Bin Wang; Han Jing Jiang

doi:10.1007/978-3-030-26766-7_36

MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System

Kai Zheng, Zhu Hong You, Lei Wang, Yi Ran Li, Yan Bin Wang, Han Jing Jiang

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

15 Scopus citations

Abstract

MicroRNAs (miRNAs) play critical roles in the development and progression of various diseases. However, traditional experimental approaches are difficult to detect potential human miRNA-disease associations from the vast amount of biological data. Therefore, computational techniques could be of significant value. In this work, we proposed a miRNA sequence similarity calculation model (MISSIM) to large-scale predict miRNA-disease associations by combined Chaos Game Representation (CGR) with Broad Learning System (BLS). In the five-cross-validation experiment, MISSIM achieved ACC of 0.8424 on the HMDD.

Original language	English
Title of host publication	Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings
Editors	De-Shuang Huang, Zhi-Kai Huang, Abir Hussain
Publisher	Springer Verlag
Pages	392-398
Number of pages	7
ISBN (Print)	9783030267650
DOIs	https://doi.org/10.1007/978-3-030-26766-7_36
State	Published - 2019
Externally published	Yes
Event	15th International Conference on Intelligent Computing, ICIC 2019 - Nanchang, China Duration: 3 Aug 2019 → 6 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11645 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Computing, ICIC 2019
Country/Territory	China
City	Nanchang
Period	3/08/19 → 6/08/19

Keywords

Broad Learning System
Chaos Game Representation
Disease
miRNAs
Sequence information

UN SDGs

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

Access to Document

10.1007/978-3-030-26766-7_36

Cite this

Zheng, K., You, Z. H., Wang, L., Li, Y. R., Wang, Y. B., & Jiang, H. J. (2019). MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System. In D.-S. Huang, Z.-K. Huang, & A. Hussain (Eds.), Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings (pp. 392-398). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11645 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-26766-7_36

Zheng, Kai ; You, Zhu Hong ; Wang, Lei et al. / MISSIM : Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System. Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings. editor / De-Shuang Huang ; Zhi-Kai Huang ; Abir Hussain. Springer Verlag, 2019. pp. 392-398 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{991573408da147c5bb049cf35e2c850b,

title = "MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System",

abstract = "MicroRNAs (miRNAs) play critical roles in the development and progression of various diseases. However, traditional experimental approaches are difficult to detect potential human miRNA-disease associations from the vast amount of biological data. Therefore, computational techniques could be of significant value. In this work, we proposed a miRNA sequence similarity calculation model (MISSIM) to large-scale predict miRNA-disease associations by combined Chaos Game Representation (CGR) with Broad Learning System (BLS). In the five-cross-validation experiment, MISSIM achieved ACC of 0.8424 on the HMDD.",

keywords = "Broad Learning System, Chaos Game Representation, Disease, miRNAs, Sequence information",

author = "Kai Zheng and You, {Zhu Hong} and Lei Wang and Li, {Yi Ran} and Wang, {Yan Bin} and Jiang, {Han Jing}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 15th International Conference on Intelligent Computing, ICIC 2019 ; Conference date: 03-08-2019 Through 06-08-2019",

year = "2019",

doi = "10.1007/978-3-030-26766-7_36",

language = "英语",

isbn = "9783030267650",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "392--398",

editor = "De-Shuang Huang and Zhi-Kai Huang and Abir Hussain",

booktitle = "Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings",

}

Zheng, K, You, ZH, Wang, L, Li, YR, Wang, YB & Jiang, HJ 2019, MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System. in D-S Huang, Z-K Huang & A Hussain (eds), Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11645 LNAI, Springer Verlag, pp. 392-398, 15th International Conference on Intelligent Computing, ICIC 2019, Nanchang, China, 3/08/19. https://doi.org/10.1007/978-3-030-26766-7_36

MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System. / Zheng, Kai; You, Zhu Hong; Wang, Lei et al.
Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings. ed. / De-Shuang Huang; Zhi-Kai Huang; Abir Hussain. Springer Verlag, 2019. p. 392-398 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11645 LNAI).

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

TY - GEN

T1 - MISSIM

T2 - 15th International Conference on Intelligent Computing, ICIC 2019

AU - Zheng, Kai

AU - You, Zhu Hong

AU - Wang, Lei

AU - Li, Yi Ran

AU - Wang, Yan Bin

AU - Jiang, Han Jing

PY - 2019

Y1 - 2019

N2 - MicroRNAs (miRNAs) play critical roles in the development and progression of various diseases. However, traditional experimental approaches are difficult to detect potential human miRNA-disease associations from the vast amount of biological data. Therefore, computational techniques could be of significant value. In this work, we proposed a miRNA sequence similarity calculation model (MISSIM) to large-scale predict miRNA-disease associations by combined Chaos Game Representation (CGR) with Broad Learning System (BLS). In the five-cross-validation experiment, MISSIM achieved ACC of 0.8424 on the HMDD.

AB - MicroRNAs (miRNAs) play critical roles in the development and progression of various diseases. However, traditional experimental approaches are difficult to detect potential human miRNA-disease associations from the vast amount of biological data. Therefore, computational techniques could be of significant value. In this work, we proposed a miRNA sequence similarity calculation model (MISSIM) to large-scale predict miRNA-disease associations by combined Chaos Game Representation (CGR) with Broad Learning System (BLS). In the five-cross-validation experiment, MISSIM achieved ACC of 0.8424 on the HMDD.

KW - Broad Learning System

KW - Chaos Game Representation

KW - Disease

KW - miRNAs

KW - Sequence information

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 398

BT - Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings

A2 - Huang, De-Shuang

A2 - Huang, Zhi-Kai

A2 - Hussain, Abir

PB - Springer Verlag

Y2 - 3 August 2019 through 6 August 2019

ER -

Zheng K, You ZH, Wang L, Li YR, Wang YB, Jiang HJ. MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System. In Huang DS, Huang ZK, Hussain A, editors, Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings. Springer Verlag. 2019. p. 392-398. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-26766-7_36

MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System

Abstract

Publication series

Conference

Keywords

UN SDGs

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