L21-iPaD: An efficient method for drug-pathway association pairs inference

Dong Qin Wang; Chun Hou Zheng; Ying Lian Gao; Jin Xing Liu; Sha Sha Wu; Jun Liang Shang

doi:10.1109/BIBM.2016.7822597

L₂₁-iPaD: An efficient method for drug-pathway association pairs inference

Dong Qin Wang
, Chun Hou Zheng
, Ying Lian Gao
, Jin Xing Liu
, Sha Sha Wu
, Jun Liang Shang

Qufu Normal University

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

2 Scopus citations

Abstract

Pathway-based drug discovery overcomes the disadvantages of the 'one drug-one target' method, which aims to find the effective drugs to act on single targets. The current method 'iPaD' identities the drug-pathway association pairs by taking the lasso-type penalty on the drug-pathway association matrix. In order to enhance the robustness of the methods and be more effective to find the novel drug-pathway association pairs, we introduce a new method named 'L_2,1-iPaD'. Compared with the iPaD method, we impose the L_2,1-norm constraint on the drug-pathway association coefficient matrix. By applying our method to a real widely datasets (CCLE dataset), we demonstrate that our method is superior to the iPaD method. And our method can obtain the smaller P-values than the iPaD method by performing permutation test to assess the significance of the identified drug-pathway association pairs. More importantly, compared with the iPaD method, our method can identify larger numbers of validated drug-pathway association pairs. The experimental results on the real dataset demonstrate the effectiveness of our method.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	664-669
Number of pages	6
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822597
State	Published - 17 Jan 2017
Externally published	Yes
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

Drug-pathway association pairs
High-throughput data
Integrative penalized matrix decomposition
L-norm
Penalized method

Access to Document

10.1109/BIBM.2016.7822597

Cite this

Wang, D. Q., Zheng, C. H., Gao, Y. L., Liu, J. X., Wu, S. S., & Shang, J. L. (2017). L₂₁-iPaD: An efficient method for drug-pathway association pairs inference. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 664-669). Article 7822597 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822597

Wang, Dong Qin ; Zheng, Chun Hou ; Gao, Ying Lian et al. / L₂₁-iPaD : An efficient method for drug-pathway association pairs inference. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 664-669 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{8b9ca24ea9a74f6cb42b87f7b52dff5e,

title = "L21-iPaD: An efficient method for drug-pathway association pairs inference",

abstract = "Pathway-based drug discovery overcomes the disadvantages of the 'one drug-one target' method, which aims to find the effective drugs to act on single targets. The current method 'iPaD' identities the drug-pathway association pairs by taking the lasso-type penalty on the drug-pathway association matrix. In order to enhance the robustness of the methods and be more effective to find the novel drug-pathway association pairs, we introduce a new method named 'L2,1-iPaD'. Compared with the iPaD method, we impose the L2,1-norm constraint on the drug-pathway association coefficient matrix. By applying our method to a real widely datasets (CCLE dataset), we demonstrate that our method is superior to the iPaD method. And our method can obtain the smaller P-values than the iPaD method by performing permutation test to assess the significance of the identified drug-pathway association pairs. More importantly, compared with the iPaD method, our method can identify larger numbers of validated drug-pathway association pairs. The experimental results on the real dataset demonstrate the effectiveness of our method.",

keywords = "Drug-pathway association pairs, High-throughput data, Integrative penalized matrix decomposition, L-norm, Penalized method",

author = "Wang, \{Dong Qin\} and Zheng, \{Chun Hou\} and Gao, \{Ying Lian\} and Liu, \{Jin Xing\} and Wu, \{Sha Sha\} and Shang, \{Jun Liang\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822597",

language = "英语",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

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

pages = "664--669",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

}

Wang, DQ, Zheng, CH, Gao, YL, Liu, JX, Wu, SS & Shang, JL 2017, L₂₁-iPaD: An efficient method for drug-pathway association pairs inference. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822597, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 664-669, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822597

L₂₁-iPaD: An efficient method for drug-pathway association pairs inference. / Wang, Dong Qin; Zheng, Chun Hou; Gao, Ying Lian et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 664-669 7822597 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

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

TY - GEN

T1 - L21-iPaD

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

AU - Wang, Dong Qin

AU - Zheng, Chun Hou

AU - Gao, Ying Lian

AU - Liu, Jin Xing

AU - Wu, Sha Sha

AU - Shang, Jun Liang

PY - 2017/1/17

Y1 - 2017/1/17

N2 - Pathway-based drug discovery overcomes the disadvantages of the 'one drug-one target' method, which aims to find the effective drugs to act on single targets. The current method 'iPaD' identities the drug-pathway association pairs by taking the lasso-type penalty on the drug-pathway association matrix. In order to enhance the robustness of the methods and be more effective to find the novel drug-pathway association pairs, we introduce a new method named 'L2,1-iPaD'. Compared with the iPaD method, we impose the L2,1-norm constraint on the drug-pathway association coefficient matrix. By applying our method to a real widely datasets (CCLE dataset), we demonstrate that our method is superior to the iPaD method. And our method can obtain the smaller P-values than the iPaD method by performing permutation test to assess the significance of the identified drug-pathway association pairs. More importantly, compared with the iPaD method, our method can identify larger numbers of validated drug-pathway association pairs. The experimental results on the real dataset demonstrate the effectiveness of our method.

AB - Pathway-based drug discovery overcomes the disadvantages of the 'one drug-one target' method, which aims to find the effective drugs to act on single targets. The current method 'iPaD' identities the drug-pathway association pairs by taking the lasso-type penalty on the drug-pathway association matrix. In order to enhance the robustness of the methods and be more effective to find the novel drug-pathway association pairs, we introduce a new method named 'L2,1-iPaD'. Compared with the iPaD method, we impose the L2,1-norm constraint on the drug-pathway association coefficient matrix. By applying our method to a real widely datasets (CCLE dataset), we demonstrate that our method is superior to the iPaD method. And our method can obtain the smaller P-values than the iPaD method by performing permutation test to assess the significance of the identified drug-pathway association pairs. More importantly, compared with the iPaD method, our method can identify larger numbers of validated drug-pathway association pairs. The experimental results on the real dataset demonstrate the effectiveness of our method.

KW - Drug-pathway association pairs

KW - High-throughput data

KW - Integrative penalized matrix decomposition

KW - L-norm

KW - Penalized method

UR - https://www.scopus.com/pages/publications/85013231711

U2 - 10.1109/BIBM.2016.7822597

DO - 10.1109/BIBM.2016.7822597

M3 - 会议稿件

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T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 664

EP - 669

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 December 2016 through 18 December 2016

ER -

Wang DQ, Zheng CH, Gao YL, Liu JX, Wu SS, Shang JL. L₂₁-iPaD: An efficient method for drug-pathway association pairs inference. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 664-669. 7822597. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822597