HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder

Wen Yu Xi; Qian Qian Ren; Jin Xing Liu; Ying Lian Gao

doi:10.1109/BIBM55620.2022.9995275

HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder

Wen Yu Xi
, Qian Qian Ren
, Jin Xing Liu
, Ying Lian Gao

Qufu Normal University

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

1 Scopus citations

Abstract

It is well known that the study of the lncRNA-disease associations (LDAs) is of great value for the diagnosis and cure of many complex diseases. However, exploring unknown LDAs is extremely difficult and expensive. Therefore, it is essential to find a more accurate and effective calculation method to predict the potential LDAs. However, most previous studies focused on designing complex similarity-based methods to predict the potential interaction between lncRNAs and diseases. In this research, combining the three biological networks of lncRNA-disease, miRNA-lncRNA and miRNA-disease, a new computing model based on heterogeneous networks and stacked autoencoder (SAE) is proposed, called HSAELDA. Then, the SAE is used to extract the comprehensive features of the lncRNA-disease pairs, the LightGBM classifier is used for training. At the same time, five-fold cross-validation (CV) is used to compare our model with some existing prediction methods. The final comparison results showed HSAELDA obtained the highest AUC value of 0.978. In conclusion, the overall prediction performance of HSAELDA has been greatly improved compared to the state-of-art models. Experimental results and case study results show that HSAELDA is an effective method for predicting potential LDAs.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Editors	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	607-612
Number of pages	6
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9995275
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: 6 Dec 2022 → 8 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/Territory	United States
City	Las Vegas
Period	6/12/22 → 8/12/22

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

LightGBM classifier
deep stacked autoencoder
disease
lncRNA
lncRNA-disease association

Access to Document

10.1109/BIBM55620.2022.9995275

Cite this

Xi, W. Y., Ren, Q. Q., Liu, J. X., & Gao, Y. L. (2022). HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. In D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (Eds.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 607-612). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9995275

Xi, Wen Yu ; Ren, Qian Qian ; Liu, Jin Xing et al. / HSAELDA : Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. editor / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 607-612 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{8bcba14b9de44cb6bc294ff92cfc8f81,

title = "HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder",

abstract = "It is well known that the study of the lncRNA-disease associations (LDAs) is of great value for the diagnosis and cure of many complex diseases. However, exploring unknown LDAs is extremely difficult and expensive. Therefore, it is essential to find a more accurate and effective calculation method to predict the potential LDAs. However, most previous studies focused on designing complex similarity-based methods to predict the potential interaction between lncRNAs and diseases. In this research, combining the three biological networks of lncRNA-disease, miRNA-lncRNA and miRNA-disease, a new computing model based on heterogeneous networks and stacked autoencoder (SAE) is proposed, called HSAELDA. Then, the SAE is used to extract the comprehensive features of the lncRNA-disease pairs, the LightGBM classifier is used for training. At the same time, five-fold cross-validation (CV) is used to compare our model with some existing prediction methods. The final comparison results showed HSAELDA obtained the highest AUC value of 0.978. In conclusion, the overall prediction performance of HSAELDA has been greatly improved compared to the state-of-art models. Experimental results and case study results show that HSAELDA is an effective method for predicting potential LDAs.",

keywords = "LightGBM classifier, deep stacked autoencoder, disease, lncRNA, lncRNA-disease association",

author = "Xi, \{Wen Yu\} and Ren, \{Qian Qian\} and Liu, \{Jin Xing\} and Gao, \{Ying Lian\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/BIBM55620.2022.9995275",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "607--612",

editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, \{Ananda M.\} and Jin Liu",

booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

}

Xi, WY, Ren, QQ, Liu, JX & Gao, YL 2022, HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. in D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (eds), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Institute of Electrical and Electronics Engineers Inc., pp. 607-612, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9995275

HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. / Xi, Wen Yu; Ren, Qian Qian; Liu, Jin Xing et al.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. ed. / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 607-612 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

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

TY - GEN

T1 - HSAELDA

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

AU - Xi, Wen Yu

AU - Ren, Qian Qian

AU - Liu, Jin Xing

AU - Gao, Ying Lian

PY - 2022

Y1 - 2022

N2 - It is well known that the study of the lncRNA-disease associations (LDAs) is of great value for the diagnosis and cure of many complex diseases. However, exploring unknown LDAs is extremely difficult and expensive. Therefore, it is essential to find a more accurate and effective calculation method to predict the potential LDAs. However, most previous studies focused on designing complex similarity-based methods to predict the potential interaction between lncRNAs and diseases. In this research, combining the three biological networks of lncRNA-disease, miRNA-lncRNA and miRNA-disease, a new computing model based on heterogeneous networks and stacked autoencoder (SAE) is proposed, called HSAELDA. Then, the SAE is used to extract the comprehensive features of the lncRNA-disease pairs, the LightGBM classifier is used for training. At the same time, five-fold cross-validation (CV) is used to compare our model with some existing prediction methods. The final comparison results showed HSAELDA obtained the highest AUC value of 0.978. In conclusion, the overall prediction performance of HSAELDA has been greatly improved compared to the state-of-art models. Experimental results and case study results show that HSAELDA is an effective method for predicting potential LDAs.

AB - It is well known that the study of the lncRNA-disease associations (LDAs) is of great value for the diagnosis and cure of many complex diseases. However, exploring unknown LDAs is extremely difficult and expensive. Therefore, it is essential to find a more accurate and effective calculation method to predict the potential LDAs. However, most previous studies focused on designing complex similarity-based methods to predict the potential interaction between lncRNAs and diseases. In this research, combining the three biological networks of lncRNA-disease, miRNA-lncRNA and miRNA-disease, a new computing model based on heterogeneous networks and stacked autoencoder (SAE) is proposed, called HSAELDA. Then, the SAE is used to extract the comprehensive features of the lncRNA-disease pairs, the LightGBM classifier is used for training. At the same time, five-fold cross-validation (CV) is used to compare our model with some existing prediction methods. The final comparison results showed HSAELDA obtained the highest AUC value of 0.978. In conclusion, the overall prediction performance of HSAELDA has been greatly improved compared to the state-of-art models. Experimental results and case study results show that HSAELDA is an effective method for predicting potential LDAs.

KW - LightGBM classifier

KW - deep stacked autoencoder

KW - disease

KW - lncRNA

KW - lncRNA-disease association

UR - https://www.scopus.com/pages/publications/85146649719

U2 - 10.1109/BIBM55620.2022.9995275

DO - 10.1109/BIBM55620.2022.9995275

M3 - 会议稿件

AN - SCOPUS:85146649719

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 607

EP - 612

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 6 December 2022 through 8 December 2022

ER -

Xi WY, Ren QQ, Liu JX, Gao YL. HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. In Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, editors, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 607-612. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9995275

HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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