MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction
In the field of cancer treatment, drug combination therapy appears to be a promising treatment strategy compared to monotherapy. Recently, plenty of computational models are gradually applied to prioritize synergistic drug combinations. However, the existing prediction models have not fully exploite...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020054 |
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| author | Lei Li Guodong Lü Chunhou Zheng Renyong Lin Yansen Su |
| author_facet | Lei Li Guodong Lü Chunhou Zheng Renyong Lin Yansen Su |
| author_sort | Lei Li |
| collection | DOAJ |
| description | In the field of cancer treatment, drug combination therapy appears to be a promising treatment strategy compared to monotherapy. Recently, plenty of computational models are gradually applied to prioritize synergistic drug combinations. However, the existing prediction models have not fully exploited the multi-way relations between drug combinations and cell lines. Besides, the number of identified drug-drug-cell line triplets is insufficient owning to the high cost of in vitro screening, which affects the ability of models to capture and utilize multi-way relations. To address this challenge, we design the multi-view hypergraph contrastive learning model, termed MHCLSyn, for synergistic drug combination prediction. First, the synergistic drug-drug-cell line triplets are formulated as a drug synergy hypergraph, and three task-specific hypergraphs are designed based on the drug synergy hypergraph. Then, we design a multi-view hypergraph contrastive learning with enhancement schemes, which allows for more expressive and discriminative node representation learning on drug synergy hypergraph. After that, the representations of nodes indicating drug-drug-cell line triplets are inputted to fully connected network for making predictions. Extensive experiments show MHCLSyn achieves better performance than state-of-the-art prediction models on benchmark datasets and is applicable to unseen drug combinations or cell lines. Case study indicates that MHCLSyn is capable of detecting potential synergistic drug combinations. |
| format | Article |
| id | doaj-art-45930274d9284b7ab0d159cd0a7f11d2 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-45930274d9284b7ab0d159cd0a7f11d22024-12-29T15:36:22ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-12-01741273128610.26599/BDMA.2024.9020054MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination PredictionLei Li0Guodong Lü1Chunhou Zheng2Renyong Lin3Yansen Su4School of Artificial Intelligence, Anhui University, Hefei 230601, ChinaState Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, ChinaSchool of Artificial Intelligence, Anhui University, Hefei 230601, ChinaState Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, ChinaSchool of Artificial Intelligence, Anhui University, Hefei 230601, ChinaIn the field of cancer treatment, drug combination therapy appears to be a promising treatment strategy compared to monotherapy. Recently, plenty of computational models are gradually applied to prioritize synergistic drug combinations. However, the existing prediction models have not fully exploited the multi-way relations between drug combinations and cell lines. Besides, the number of identified drug-drug-cell line triplets is insufficient owning to the high cost of in vitro screening, which affects the ability of models to capture and utilize multi-way relations. To address this challenge, we design the multi-view hypergraph contrastive learning model, termed MHCLSyn, for synergistic drug combination prediction. First, the synergistic drug-drug-cell line triplets are formulated as a drug synergy hypergraph, and three task-specific hypergraphs are designed based on the drug synergy hypergraph. Then, we design a multi-view hypergraph contrastive learning with enhancement schemes, which allows for more expressive and discriminative node representation learning on drug synergy hypergraph. After that, the representations of nodes indicating drug-drug-cell line triplets are inputted to fully connected network for making predictions. Extensive experiments show MHCLSyn achieves better performance than state-of-the-art prediction models on benchmark datasets and is applicable to unseen drug combinations or cell lines. Case study indicates that MHCLSyn is capable of detecting potential synergistic drug combinations.https://www.sciopen.com/article/10.26599/BDMA.2024.9020054synergistic drug combinationscell linesmulti-way relationsmulti-view hypergraph contrastive learning |
| spellingShingle | Lei Li Guodong Lü Chunhou Zheng Renyong Lin Yansen Su MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction Big Data Mining and Analytics synergistic drug combinations cell lines multi-way relations multi-view hypergraph contrastive learning |
| title | MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction |
| title_full | MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction |
| title_fullStr | MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction |
| title_full_unstemmed | MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction |
| title_short | MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction |
| title_sort | mhclsyn multi view hypergraph contrastive learning for synergistic drug combination prediction |
| topic | synergistic drug combinations cell lines multi-way relations multi-view hypergraph contrastive learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020054 |
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