LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction

Xu Ran Dou; Wen Yu Xi; Tian Ru Wu; Cui Na Jiao; Jin Xing Liu; Ying Lian Gao

doi:10.1007/978-981-99-4749-2_25

LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction

Xu Ran Dou
, Wen Yu Xi
, Tian Ru Wu
, Cui Na Jiao
, Jin Xing Liu
, Ying Lian Gao

Qufu Normal University

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

Abstract

Since the abnormal expressions of microRNAs (miRNAs) were likely to induce human diseases and traditional biological methods for predicting miRNA-disease associations (MDAs) are costly and time-consuming, it is a great necessity to create computational methods for MDA prediction. In this article, a computational deep learning-based method called predicting miRNA-disease associations through Light Gradient Boosting Machine (LightGBM) with attributed network construction (LANCMDA) is put forward. Specifically, the integrated features obtained from multi-proximity matrices fusion are learnt by sparse autoencoder and then were trained via LightGBM classifier. What’s more, a 5-fold cross-validation is applied to evaluate the performance of this method and the area under curve (AUC) value is 0.9537. It shows that the LANCMDA is very promising. Lung neoplasms is also selected to conduct case study and 95% of the top 20 predicted miRNAs are verified by databases, which shows that LANCMDA is reliable to predict MDAs.

Original language	English
Title of host publication	Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings
Editors	De-Shuang Huang, Prashan Premaratne, Baohua Jin, Boyang Qu, Kang-Hyun Jo, Abir Hussain
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	291-299
Number of pages	9
ISBN (Print)	9789819947485
DOIs	https://doi.org/10.1007/978-981-99-4749-2_25
State	Published - 2023
Externally published	Yes
Event	19th International Conference on Intelligent Computing, ICIC 2023 - Zhengzhou, China Duration: 10 Aug 2023 → 13 Aug 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14088 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Intelligent Computing, ICIC 2023
Country/Territory	China
City	Zhengzhou
Period	10/08/23 → 13/08/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Attribute and Structure Features Fusion
Deep Learning
MiRNA-Disease Association

Access to Document

10.1007/978-981-99-4749-2_25

Cite this

Dou, X. R., Xi, W. Y., Wu, T. R., Jiao, C. N., Liu, J. X., & Gao, Y. L. (2023). LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. In D.-S. Huang, P. Premaratne, B. Jin, B. Qu, K.-H. Jo, & A. Hussain (Eds.), Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings (pp. 291-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14088 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-4749-2_25

Dou, Xu Ran ; Xi, Wen Yu ; Wu, Tian Ru et al. / LANCMDA : Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings. editor / De-Shuang Huang ; Prashan Premaratne ; Baohua Jin ; Boyang Qu ; Kang-Hyun Jo ; Abir Hussain. Springer Science and Business Media Deutschland GmbH, 2023. pp. 291-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{27d76d5692e4404584aaf045cd4621fb,

title = "LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction",

abstract = "Since the abnormal expressions of microRNAs (miRNAs) were likely to induce human diseases and traditional biological methods for predicting miRNA-disease associations (MDAs) are costly and time-consuming, it is a great necessity to create computational methods for MDA prediction. In this article, a computational deep learning-based method called predicting miRNA-disease associations through Light Gradient Boosting Machine (LightGBM) with attributed network construction (LANCMDA) is put forward. Specifically, the integrated features obtained from multi-proximity matrices fusion are learnt by sparse autoencoder and then were trained via LightGBM classifier. What{\textquoteright}s more, a 5-fold cross-validation is applied to evaluate the performance of this method and the area under curve (AUC) value is 0.9537. It shows that the LANCMDA is very promising. Lung neoplasms is also selected to conduct case study and 95\% of the top 20 predicted miRNAs are verified by databases, which shows that LANCMDA is reliable to predict MDAs.",

keywords = "Attribute and Structure Features Fusion, Deep Learning, MiRNA-Disease Association",

author = "Dou, \{Xu Ran\} and Xi, \{Wen Yu\} and Wu, \{Tian Ru\} and Jiao, \{Cui Na\} and Liu, \{Jin Xing\} and Gao, \{Ying Lian\}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 19th International Conference on Intelligent Computing, ICIC 2023 ; Conference date: 10-08-2023 Through 13-08-2023",

year = "2023",

doi = "10.1007/978-981-99-4749-2\_25",

language = "英语",

isbn = "9789819947485",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "291--299",

editor = "De-Shuang Huang and Prashan Premaratne and Baohua Jin and Boyang Qu and Kang-Hyun Jo and Abir Hussain",

booktitle = "Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings",

}

Dou, XR, Xi, WY, Wu, TR, Jiao, CN, Liu, JX & Gao, YL 2023, LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. in D-S Huang, P Premaratne, B Jin, B Qu, K-H Jo & A Hussain (eds), Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14088 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 291-299, 19th International Conference on Intelligent Computing, ICIC 2023, Zhengzhou, China, 10/08/23. https://doi.org/10.1007/978-981-99-4749-2_25

LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. / Dou, Xu Ran; Xi, Wen Yu; Wu, Tian Ru et al.
Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings. ed. / De-Shuang Huang; Prashan Premaratne; Baohua Jin; Boyang Qu; Kang-Hyun Jo; Abir Hussain. Springer Science and Business Media Deutschland GmbH, 2023. p. 291-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14088 LNCS).

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

TY - GEN

T1 - LANCMDA

T2 - 19th International Conference on Intelligent Computing, ICIC 2023

AU - Dou, Xu Ran

AU - Xi, Wen Yu

AU - Wu, Tian Ru

AU - Jiao, Cui Na

AU - Liu, Jin Xing

AU - Gao, Ying Lian

PY - 2023

Y1 - 2023

N2 - Since the abnormal expressions of microRNAs (miRNAs) were likely to induce human diseases and traditional biological methods for predicting miRNA-disease associations (MDAs) are costly and time-consuming, it is a great necessity to create computational methods for MDA prediction. In this article, a computational deep learning-based method called predicting miRNA-disease associations through Light Gradient Boosting Machine (LightGBM) with attributed network construction (LANCMDA) is put forward. Specifically, the integrated features obtained from multi-proximity matrices fusion are learnt by sparse autoencoder and then were trained via LightGBM classifier. What’s more, a 5-fold cross-validation is applied to evaluate the performance of this method and the area under curve (AUC) value is 0.9537. It shows that the LANCMDA is very promising. Lung neoplasms is also selected to conduct case study and 95% of the top 20 predicted miRNAs are verified by databases, which shows that LANCMDA is reliable to predict MDAs.

AB - Since the abnormal expressions of microRNAs (miRNAs) were likely to induce human diseases and traditional biological methods for predicting miRNA-disease associations (MDAs) are costly and time-consuming, it is a great necessity to create computational methods for MDA prediction. In this article, a computational deep learning-based method called predicting miRNA-disease associations through Light Gradient Boosting Machine (LightGBM) with attributed network construction (LANCMDA) is put forward. Specifically, the integrated features obtained from multi-proximity matrices fusion are learnt by sparse autoencoder and then were trained via LightGBM classifier. What’s more, a 5-fold cross-validation is applied to evaluate the performance of this method and the area under curve (AUC) value is 0.9537. It shows that the LANCMDA is very promising. Lung neoplasms is also selected to conduct case study and 95% of the top 20 predicted miRNAs are verified by databases, which shows that LANCMDA is reliable to predict MDAs.

KW - Attribute and Structure Features Fusion

KW - Deep Learning

KW - MiRNA-Disease Association

UR - https://www.scopus.com/pages/publications/85174811057

U2 - 10.1007/978-981-99-4749-2_25

DO - 10.1007/978-981-99-4749-2_25

M3 - 会议稿件

AN - SCOPUS:85174811057

SN - 9789819947485

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

SP - 291

EP - 299

BT - Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings

A2 - Huang, De-Shuang

A2 - Premaratne, Prashan

A2 - Jin, Baohua

A2 - Qu, Boyang

A2 - Jo, Kang-Hyun

A2 - Hussain, Abir

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 10 August 2023 through 13 August 2023

ER -

Dou XR, Xi WY, Wu TR, Jiao CN, Liu JX, Gao YL. LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. In Huang DS, Premaratne P, Jin B, Qu B, Jo KH, Hussain A, editors, Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 291-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-4749-2_25

LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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