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

China University of Mining and Technology
Xinjiang Technical Institute of Physics and Chemistry

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

15 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding
编辑	De-Shuang Huang, Zhi-Kai Huang, Abir Hussain
出版商	Springer Verlag
页	392-398
页数	7
ISBN（印刷版）	9783030267650
DOI	https://doi.org/10.1007/978-3-030-26766-7_36
出版状态	已出版 - 2019
已对外发布	是
活动	15th International Conference on Intelligent Computing, ICIC 2019 - Nanchang, 中国期限: 3 8月 2019 → 6 8月 2019

出版系列

姓名	Lecture Notes in Computer Science
卷	11645 LNAI
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	15th International Conference on Intelligent Computing, ICIC 2019
国家/地区	中国
市	Nanchang
时期	3/08/19 → 6/08/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 D.-S. Huang, Z.-K. Huang, & A. Hussain (编辑), Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding (页码 392-398). (Lecture Notes in Computer Science; 卷 11645 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-26766-7_36

Zheng, Kai ; You, Zhu Hong ; Wang, Lei 等. / MISSIM : Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System. Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding. 编辑 / De-Shuang Huang ; Zhi-Kai Huang ; Abir Hussain. Springer Verlag, 2019. 页码 392-398 (Lecture Notes in Computer Science).

@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, Sequence information, miRNAs",

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",

publisher = "Springer Verlag",

pages = "392--398",

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

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

}

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. 在 D-S Huang, Z-K Huang & A Hussain (编辑), Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding. Lecture Notes in Computer Science, 卷 11645 LNAI, Springer Verlag, 页码 392-398, 15th International Conference on Intelligent Computing, ICIC 2019, Nanchang, 中国, 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 等.
Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding. 编辑 / De-Shuang Huang; Zhi-Kai Huang; Abir Hussain. Springer Verlag, 2019. 页码 392-398 (Lecture Notes in Computer Science; 卷 11645 LNAI).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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 - Sequence information

KW - miRNAs

UR - https://www.scopus.com/pages/publications/85070705728

U2 - 10.1007/978-3-030-26766-7_36

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

M3 - 会议稿件

AN - SCOPUS:85070705728

SN - 9783030267650

T3 - Lecture Notes in Computer Science

SP - 392

EP - 398

BT - Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding

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. 在 Huang DS, Huang ZK, Hussain A, 编辑, Intelligent Computing - 15th International Conference, ICIC 2019, Proceeding. Springer Verlag. 2019. 页码 392-398. (Lecture Notes in Computer Science). 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

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此