A network embedding based approach to drug-target interaction prediction using additional implicit networks

Han Zhang; Chengbin Hou; David McDonald; Shan He

doi:10.1007/978-3-030-86362-3_40

A network embedding based approach to drug-target interaction prediction using additional implicit networks

Han Zhang, Chengbin Hou, David McDonald, Shan He^*

^*Corresponding author for this work

Computer Science

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

Abstract

Identifying novel drug-target interactions (DTIs) is a crucial step in drug discovery. Since experimentally determining DTIs is expensive and time-consuming, it becomes popular to employ computational methods for providing promising candidate DTIs. However, in the existing computational methods, the drug implicit network and target implicit network constructed from a DTI network (a bipartite network) have been ignored in the DTI prediction problem, while such implicit networks constructed from a bipartite network have been proven useful in other problems, e.g., the link prediction task in a bipartite network. Motivated by that, we propose a novel DTI prediction method which considers the implicit networks in addition to drug structure similarity network and target sequence similarity network. The experiments over five real-world DTI datasets demonstrate the competitive performance of the proposed method compared to the state-of-the-art methods. The code is available at https://github.com/BrisksHan/NE-DTIP.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2021
Subtitle of host publication	30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I
Editors	Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
Publisher	Springer
Pages	491-503
Number of pages	13
Edition	1
ISBN (Electronic)	9783030863623
ISBN (Print)	9783030863616
DOIs	https://doi.org/10.1007/978-3-030-86362-3_40
Publication status	Published - 7 Sept 2021
Event	30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online Duration: 14 Sept 2021 → 17 Sept 2021

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	1289
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Artificial Neural Networks, ICANN 2021
City	Virtual, Online
Period	14/09/21 → 17/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Drug-Target Interaction Prediction
Implicit networks
Network embedding
Network topology

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-86362-3_40

Cite this

Zhang, H., Hou, C., McDonald, D., & He, S. (2021). A network embedding based approach to drug-target interaction prediction using additional implicit networks. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I (1 ed., pp. 491-503). (Lecture Notes in Computer Science; Vol. 1289). Springer. https://doi.org/10.1007/978-3-030-86362-3_40

Zhang, Han ; Hou, Chengbin ; McDonald, David et al. / A network embedding based approach to drug-target interaction prediction using additional implicit networks. Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. editor / Igor Farkaš ; Paolo Masulli ; Sebastian Otte ; Stefan Wermter. 1. ed. Springer, 2021. pp. 491-503 (Lecture Notes in Computer Science).

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title = "A network embedding based approach to drug-target interaction prediction using additional implicit networks",

abstract = "Identifying novel drug-target interactions (DTIs) is a crucial step in drug discovery. Since experimentally determining DTIs is expensive and time-consuming, it becomes popular to employ computational methods for providing promising candidate DTIs. However, in the existing computational methods, the drug implicit network and target implicit network constructed from a DTI network (a bipartite network) have been ignored in the DTI prediction problem, while such implicit networks constructed from a bipartite network have been proven useful in other problems, e.g., the link prediction task in a bipartite network. Motivated by that, we propose a novel DTI prediction method which considers the implicit networks in addition to drug structure similarity network and target sequence similarity network. The experiments over five real-world DTI datasets demonstrate the competitive performance of the proposed method compared to the state-of-the-art methods. The code is available at https://github.com/BrisksHan/NE-DTIP.",

keywords = "Drug-Target Interaction Prediction, Implicit networks, Network embedding, Network topology",

author = "Han Zhang and Chengbin Hou and David McDonald and Shan He",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 30th International Conference on Artificial Neural Networks, ICANN 2021 ; Conference date: 14-09-2021 Through 17-09-2021",

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Zhang, H, Hou, C, McDonald, D & He, S 2021, A network embedding based approach to drug-target interaction prediction using additional implicit networks. in I Farkaš, P Masulli, S Otte & S Wermter (eds), Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. 1 edn, Lecture Notes in Computer Science, vol. 1289, Springer, pp. 491-503, 30th International Conference on Artificial Neural Networks, ICANN 2021, Virtual, Online, 14/09/21. https://doi.org/10.1007/978-3-030-86362-3_40

A network embedding based approach to drug-target interaction prediction using additional implicit networks. / Zhang, Han; Hou, Chengbin; McDonald, David et al.
Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. ed. / Igor Farkaš; Paolo Masulli; Sebastian Otte; Stefan Wermter. 1. ed. Springer, 2021. p. 491-503 (Lecture Notes in Computer Science; Vol. 1289).

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

TY - GEN

T1 - A network embedding based approach to drug-target interaction prediction using additional implicit networks

AU - Zhang, Han

AU - Hou, Chengbin

AU - McDonald, David

AU - He, Shan

PY - 2021/9/7

Y1 - 2021/9/7

N2 - Identifying novel drug-target interactions (DTIs) is a crucial step in drug discovery. Since experimentally determining DTIs is expensive and time-consuming, it becomes popular to employ computational methods for providing promising candidate DTIs. However, in the existing computational methods, the drug implicit network and target implicit network constructed from a DTI network (a bipartite network) have been ignored in the DTI prediction problem, while such implicit networks constructed from a bipartite network have been proven useful in other problems, e.g., the link prediction task in a bipartite network. Motivated by that, we propose a novel DTI prediction method which considers the implicit networks in addition to drug structure similarity network and target sequence similarity network. The experiments over five real-world DTI datasets demonstrate the competitive performance of the proposed method compared to the state-of-the-art methods. The code is available at https://github.com/BrisksHan/NE-DTIP.

AB - Identifying novel drug-target interactions (DTIs) is a crucial step in drug discovery. Since experimentally determining DTIs is expensive and time-consuming, it becomes popular to employ computational methods for providing promising candidate DTIs. However, in the existing computational methods, the drug implicit network and target implicit network constructed from a DTI network (a bipartite network) have been ignored in the DTI prediction problem, while such implicit networks constructed from a bipartite network have been proven useful in other problems, e.g., the link prediction task in a bipartite network. Motivated by that, we propose a novel DTI prediction method which considers the implicit networks in addition to drug structure similarity network and target sequence similarity network. The experiments over five real-world DTI datasets demonstrate the competitive performance of the proposed method compared to the state-of-the-art methods. The code is available at https://github.com/BrisksHan/NE-DTIP.

KW - Drug-Target Interaction Prediction

KW - Implicit networks

KW - Network embedding

KW - Network topology

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T3 - Lecture Notes in Computer Science

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BT - Artificial Neural Networks and Machine Learning – ICANN 2021

A2 - Farkaš, Igor

A2 - Masulli, Paolo

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A2 - Wermter, Stefan

PB - Springer

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ER -

Zhang H, Hou C, McDonald D, He S. A network embedding based approach to drug-target interaction prediction using additional implicit networks. In Farkaš I, Masulli P, Otte S, Wermter S, editors, Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. 1 ed. Springer. 2021. p. 491-503. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-86362-3_40

A network embedding based approach to drug-target interaction prediction using additional implicit networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this