RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker

Yuting Lu; Yan Wang; Nan Sheng; Hao Wang; Yuan Fu; Yuan Tian

doi:10.1109/BIBM55620.2022.9995215

RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker

Yuting Lu, Yan Wang^*, Nan Sheng, Hao Wang, Yuan Fu, Yuan Tian

^*Corresponding author for this work

Institute of Biological, Environmental & Rural Sciences (IBERS)

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

Abstract

Pancreatic cancer is a malignant cancer with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding cancer biomarkers. Most of the existing network-based methods do not study RNA, rely on prior or mutation information, or can only achieve classification tasks. In this paper, wep ropose a method combining Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. We first construct a regulation network containing three types RNAs. Then, Relational Graph Convolutional Network is used to learn the node representation. Finally, we combine the idea of Deep Q-Network to prioritize each RNA with the network controllability theory. We train RDDriver on three simulated small networks, and calculate the average score after applying the model parameters to the regulation networks. To demonstrate the effectiveness of the method, we compare RDDriver with other eight methods based on the approximate cancer drivers benchmark RNAs.

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	IEEE Press
Pages	497-502
Number of pages	6
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9995215
Publication status	Published - 02 Jan 2023
Event	Bioinformatics and Biomedicine - Caesars Palace, Las Vegas, NV, USA & World Hotel Grand Jiaxing, Hunan, PR China, Las Vegas, United States of America Duration: 06 Dec 2022 → 08 Dec 2022 Conference number: 2022 https://ieeebibm.org/BIBM2022/

Publication series

Name	2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publisher	IEEE Press

Conference

Conference	Bioinformatics and Biomedicine
Abbreviated title	BIBM
Country/Territory	United States of America
City	Las Vegas
Period	06 Dec 2022 → 08 Dec 2022
Internet address	https://ieeebibm.org/BIBM2022/

Keywords

network controllability
pancreatic cancer biomarkers
Relational Graph Convolutional Network
transcriptional regulation network

UN SDGs

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

Access to Document

10.1109/BIBM55620.2022.9995215

Permanent link

Persistent link

Cite this

Lu, Y., Wang, Y., Sheng, N., Wang, H., Fu, Y., & Tian, Y. (2023). RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker. 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. 497-502). (2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)). IEEE Press. https://doi.org/10.1109/BIBM55620.2022.9995215

Lu, Yuting ; Wang, Yan ; Sheng, Nan et al. / RDDriver : A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker. 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. IEEE Press, 2023. pp. 497-502 (2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)).

@inproceedings{634259f458d34b21a237ccff78af6882,

title = "RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker",

abstract = "Pancreatic cancer is a malignant cancer with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding cancer biomarkers. Most of the existing network-based methods do not study RNA, rely on prior or mutation information, or can only achieve classification tasks. In this paper, wep ropose a method combining Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. We first construct a regulation network containing three types RNAs. Then, Relational Graph Convolutional Network is used to learn the node representation. Finally, we combine the idea of Deep Q-Network to prioritize each RNA with the network controllability theory. We train RDDriver on three simulated small networks, and calculate the average score after applying the model parameters to the regulation networks. To demonstrate the effectiveness of the method, we compare RDDriver with other eight methods based on the approximate cancer drivers benchmark RNAs.",

keywords = "network controllability, pancreatic cancer biomarkers, Relational Graph Convolutional Network, transcriptional regulation network",

author = "Yuting Lu and Yan Wang and Nan Sheng and Hao Wang and Yuan Fu and Yuan Tian",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China (No. 62072212), the Development Project of Jilin Province of China (Nos. 20220508125RC, 20200403172SF, 20210508060RQ). Publisher Copyright: {\textcopyright} 2022 IEEE.; Bioinformatics and Biomedicine, BIBM ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2023",

month = jan,

day = "2",

doi = "10.1109/BIBM55620.2022.9995215",

language = "English",

series = "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",

publisher = "IEEE Press",

pages = "497--502",

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",

address = "United States of America",

url = "https://ieeebibm.org/BIBM2022/",

}

Lu, Y, Wang, Y, Sheng, N, Wang, H, Fu, Y & Tian, Y 2023, RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker. 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. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE Press, pp. 497-502, Bioinformatics and Biomedicine, Las Vegas, United States of America, 06 Dec 2022. https://doi.org/10.1109/BIBM55620.2022.9995215

RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker. / Lu, Yuting; Wang, Yan; Sheng, Nan 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. IEEE Press, 2023. p. 497-502 (2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - RDDriver

T2 - Bioinformatics and Biomedicine

AU - Lu, Yuting

AU - Wang, Yan

AU - Sheng, Nan

AU - Wang, Hao

AU - Fu, Yuan

AU - Tian, Yuan

N1 - Conference code: 2022

PY - 2023/1/2

Y1 - 2023/1/2

N2 - Pancreatic cancer is a malignant cancer with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding cancer biomarkers. Most of the existing network-based methods do not study RNA, rely on prior or mutation information, or can only achieve classification tasks. In this paper, wep ropose a method combining Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. We first construct a regulation network containing three types RNAs. Then, Relational Graph Convolutional Network is used to learn the node representation. Finally, we combine the idea of Deep Q-Network to prioritize each RNA with the network controllability theory. We train RDDriver on three simulated small networks, and calculate the average score after applying the model parameters to the regulation networks. To demonstrate the effectiveness of the method, we compare RDDriver with other eight methods based on the approximate cancer drivers benchmark RNAs.

AB - Pancreatic cancer is a malignant cancer with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding cancer biomarkers. Most of the existing network-based methods do not study RNA, rely on prior or mutation information, or can only achieve classification tasks. In this paper, wep ropose a method combining Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. We first construct a regulation network containing three types RNAs. Then, Relational Graph Convolutional Network is used to learn the node representation. Finally, we combine the idea of Deep Q-Network to prioritize each RNA with the network controllability theory. We train RDDriver on three simulated small networks, and calculate the average score after applying the model parameters to the regulation networks. To demonstrate the effectiveness of the method, we compare RDDriver with other eight methods based on the approximate cancer drivers benchmark RNAs.

KW - network controllability

KW - pancreatic cancer biomarkers

KW - Relational Graph Convolutional Network

KW - transcriptional regulation network

UR - http://www.scopus.com/inward/record.url?scp=85146673447&partnerID=8YFLogxK

U2 - 10.1109/BIBM55620.2022.9995215

DO - 10.1109/BIBM55620.2022.9995215

M3 - Conference Proceeding (Non-Journal item)

AN - SCOPUS:85146673447

T3 - 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

SP - 497

EP - 502

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 - IEEE Press

Y2 - 6 December 2022 through 8 December 2022

ER -

Lu Y, Wang Y, Sheng N, Wang H, Fu Y, Tian Y. RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker. 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. IEEE Press. 2023. p. 497-502. (2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)). Epub 2022 Dec 8. doi: 10.1109/BIBM55620.2022.9995215

RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Permanent link

Other files and links

Fingerprint

Cite this