MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction

Bo Wei Zhao; Lun Hu; Peng Wei Hu; Zhu Hong You; Xiao Rui Su; Dong Xu Li; Zhan Heng Chen; Ping Zhang

doi:10.1007/978-3-031-13829-4_39

MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction

Bo Wei Zhao, Lun Hu, Peng Wei Hu, Zhu Hong You, Xiao Rui Su, Dong Xu Li, Zhan Heng Chen, Ping Zhang

School of Computer Science

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

1 Scopus citations

Abstract

Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.

Original language	English
Title of host publication	Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings
Editors	De-Shuang Huang, Kang-Hyun Jo, Junfeng Jing, Prashan Premaratne, Vitoantonio Bevilacqua, Abir Hussain
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	451-459
Number of pages	9
ISBN (Print)	9783031138287
DOIs	https://doi.org/10.1007/978-3-031-13829-4_39
State	Published - 2022
Event	18th International Conference on Intelligent Computing, ICIC 2022 - Xi'an, China Duration: 7 Aug 2022 → 11 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13394 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Intelligent Computing, ICIC 2022
Country/Territory	China
City	Xi'an
Period	7/08/22 → 11/08/22

Keywords

Computational prediction
Drug repositioning
Drugs
DTIs
Targets

Access to Document

10.1007/978-3-031-13829-4_39

Cite this

Zhao, B. W., Hu, L., Hu, P. W., You, Z. H., Su, X. R., Li, D. X., Chen, Z. H., & Zhang, P. (2022). MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. In D.-S. Huang, K.-H. Jo, J. Jing, P. Premaratne, V. Bevilacqua, & A. Hussain (Eds.), Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings (pp. 451-459). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13394 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-13829-4_39

Zhao, Bo Wei ; Hu, Lun ; Hu, Peng Wei et al. / MRLDTI : A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings. editor / De-Shuang Huang ; Kang-Hyun Jo ; Junfeng Jing ; Prashan Premaratne ; Vitoantonio Bevilacqua ; Abir Hussain. Springer Science and Business Media Deutschland GmbH, 2022. pp. 451-459 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{76b9b24231d047648a809704d007a55a,

title = "MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction",

abstract = "Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.",

keywords = "Computational prediction, Drug repositioning, Drugs, DTIs, Targets",

author = "Zhao, {Bo Wei} and Lun Hu and Hu, {Peng Wei} and You, {Zhu Hong} and Su, {Xiao Rui} and Li, {Dong Xu} and Chen, {Zhan Heng} and Ping Zhang",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 18th International Conference on Intelligent Computing, ICIC 2022 ; Conference date: 07-08-2022 Through 11-08-2022",

year = "2022",

doi = "10.1007/978-3-031-13829-4_39",

language = "英语",

isbn = "9783031138287",

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 = "451--459",

editor = "De-Shuang Huang and Kang-Hyun Jo and Junfeng Jing and Prashan Premaratne and Vitoantonio Bevilacqua and Abir Hussain",

booktitle = "Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings",

}

Zhao, BW, Hu, L, Hu, PW, You, ZH, Su, XR, Li, DX, Chen, ZH & Zhang, P 2022, MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. in D-S Huang, K-H Jo, J Jing, P Premaratne, V Bevilacqua & A Hussain (eds), Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13394 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 451-459, 18th International Conference on Intelligent Computing, ICIC 2022, Xi'an, China, 7/08/22. https://doi.org/10.1007/978-3-031-13829-4_39

MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. / Zhao, Bo Wei; Hu, Lun; Hu, Peng Wei et al.
Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings. ed. / De-Shuang Huang; Kang-Hyun Jo; Junfeng Jing; Prashan Premaratne; Vitoantonio Bevilacqua; Abir Hussain. Springer Science and Business Media Deutschland GmbH, 2022. p. 451-459 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13394 LNCS).

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

TY - GEN

T1 - MRLDTI

T2 - 18th International Conference on Intelligent Computing, ICIC 2022

AU - Zhao, Bo Wei

AU - Hu, Lun

AU - Hu, Peng Wei

AU - You, Zhu Hong

AU - Su, Xiao Rui

AU - Li, Dong Xu

AU - Chen, Zhan Heng

AU - Zhang, Ping

PY - 2022

Y1 - 2022

N2 - Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.

AB - Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.

KW - Computational prediction

KW - Drug repositioning

KW - Drugs

KW - DTIs

KW - Targets

UR - http://www.scopus.com/inward/record.url?scp=85139228631&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-13829-4_39

DO - 10.1007/978-3-031-13829-4_39

M3 - 会议稿件

AN - SCOPUS:85139228631

SN - 9783031138287

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

SP - 451

EP - 459

BT - Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings

A2 - Huang, De-Shuang

A2 - Jo, Kang-Hyun

A2 - Jing, Junfeng

A2 - Premaratne, Prashan

A2 - Bevilacqua, Vitoantonio

A2 - Hussain, Abir

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 7 August 2022 through 11 August 2022

ER -

Zhao BW, Hu L, Hu PW, You ZH, Su XR, Li DX et al. MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. In Huang DS, Jo KH, Jing J, Premaratne P, Bevilacqua V, Hussain A, editors, Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 451-459. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-13829-4_39

MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this