DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks

Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Featur...
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Autor*in:	Xu, Mengdie [verfasserIn] Zhao, Xinwei Wang, Jingyu Feng, Wei Wen, Naifeng Wang, Chunyu Wang, Junjie Liu, Yun Zhao, Lingling

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Drug combination Synergistic effect Deep learning Dual-feature fusion

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Journal of cheminformatics - London : BioMed Central, 2009, 15(2023), 1 vom: 16. März
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:1 ; day:16 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s13321-023-00690-3

Katalog-ID:	SPR049715917

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allfields	10.1186/s13321-023-00690-3 doi (DE-627)SPR049715917 (SPR)s13321-023-00690-3-e DE-627 ger DE-627 rakwb eng Xu, Mengdie verfasserin aut DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. Drug combination (dpeaa)DE-He213 Synergistic effect (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dual-feature fusion (dpeaa)DE-He213 Zhao, Xinwei aut Wang, Jingyu aut Feng, Wei aut Wen, Naifeng aut Wang, Chunyu aut Wang, Junjie aut Liu, Yun aut Zhao, Lingling aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 15(2023), 1 vom: 16. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:15 year:2023 number:1 day:16 month:03 https://dx.doi.org/10.1186/s13321-023-00690-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 16 03
spelling	10.1186/s13321-023-00690-3 doi (DE-627)SPR049715917 (SPR)s13321-023-00690-3-e DE-627 ger DE-627 rakwb eng Xu, Mengdie verfasserin aut DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. Drug combination (dpeaa)DE-He213 Synergistic effect (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dual-feature fusion (dpeaa)DE-He213 Zhao, Xinwei aut Wang, Jingyu aut Feng, Wei aut Wen, Naifeng aut Wang, Chunyu aut Wang, Junjie aut Liu, Yun aut Zhao, Lingling aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 15(2023), 1 vom: 16. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:15 year:2023 number:1 day:16 month:03 https://dx.doi.org/10.1186/s13321-023-00690-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 16 03
allfields_unstemmed	10.1186/s13321-023-00690-3 doi (DE-627)SPR049715917 (SPR)s13321-023-00690-3-e DE-627 ger DE-627 rakwb eng Xu, Mengdie verfasserin aut DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. Drug combination (dpeaa)DE-He213 Synergistic effect (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dual-feature fusion (dpeaa)DE-He213 Zhao, Xinwei aut Wang, Jingyu aut Feng, Wei aut Wen, Naifeng aut Wang, Chunyu aut Wang, Junjie aut Liu, Yun aut Zhao, Lingling aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 15(2023), 1 vom: 16. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:15 year:2023 number:1 day:16 month:03 https://dx.doi.org/10.1186/s13321-023-00690-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 16 03
allfieldsGer	10.1186/s13321-023-00690-3 doi (DE-627)SPR049715917 (SPR)s13321-023-00690-3-e DE-627 ger DE-627 rakwb eng Xu, Mengdie verfasserin aut DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. Drug combination (dpeaa)DE-He213 Synergistic effect (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dual-feature fusion (dpeaa)DE-He213 Zhao, Xinwei aut Wang, Jingyu aut Feng, Wei aut Wen, Naifeng aut Wang, Chunyu aut Wang, Junjie aut Liu, Yun aut Zhao, Lingling aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 15(2023), 1 vom: 16. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:15 year:2023 number:1 day:16 month:03 https://dx.doi.org/10.1186/s13321-023-00690-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 16 03
allfieldsSound	10.1186/s13321-023-00690-3 doi (DE-627)SPR049715917 (SPR)s13321-023-00690-3-e DE-627 ger DE-627 rakwb eng Xu, Mengdie verfasserin aut DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. Drug combination (dpeaa)DE-He213 Synergistic effect (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dual-feature fusion (dpeaa)DE-He213 Zhao, Xinwei aut Wang, Jingyu aut Feng, Wei aut Wen, Naifeng aut Wang, Chunyu aut Wang, Junjie aut Liu, Yun aut Zhao, Lingling aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 15(2023), 1 vom: 16. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:15 year:2023 number:1 day:16 month:03 https://dx.doi.org/10.1186/s13321-023-00690-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 16 03
language	English
source	Enthalten in Journal of cheminformatics 15(2023), 1 vom: 16. März volume:15 year:2023 number:1 day:16 month:03
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topic_facet	Drug combination Synergistic effect Deep learning Dual-feature fusion
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authorswithroles_txt_mv	Xu, Mengdie @@aut@@ Zhao, Xinwei @@aut@@ Wang, Jingyu @@aut@@ Feng, Wei @@aut@@ Wen, Naifeng @@aut@@ Wang, Chunyu @@aut@@ Wang, Junjie @@aut@@ Liu, Yun @@aut@@ Zhao, Lingling @@aut@@
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author	Xu, Mengdie
spellingShingle	Xu, Mengdie misc Drug combination misc Synergistic effect misc Deep learning misc Dual-feature fusion DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
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topic_title	DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks Drug combination (dpeaa)DE-He213 Synergistic effect (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Dual-feature fusion (dpeaa)DE-He213
topic	misc Drug combination misc Synergistic effect misc Deep learning misc Dual-feature fusion
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title	DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
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title_full	DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
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title_sort	dffndds: prediction of synergistic drug combinations with dual feature fusion networks
title_auth	DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
abstract	Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. © The Author(s) 2023
abstractGer	Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. © The Author(s) 2023
abstract_unstemmed	Abstract Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations. © The Author(s) 2023
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title_short	DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
url	https://dx.doi.org/10.1186/s13321-023-00690-3
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author2	Zhao, Xinwei Wang, Jingyu Feng, Wei Wen, Naifeng Wang, Chunyu Wang, Junjie Liu, Yun Zhao, Lingling
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