Kernel-based prediction of a synergistic drug combination Article Swipe
Jun ZHANG
,
Rui Yuan
,
Shilong Chen
,
Yongcui Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1360/ssv-2023-0033
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1360/ssv-2023-0033
Complex diseases, such as cancer, often exhibit resistance to single drug therapy because of their heterogeneity and complex metabolic pathways. Combination therapy is an efficient strategy to overcome drug resistance. As experimental screenings consume considerable resources and have low efficacy, the computational method is a good alternative. Thus, this article proposes a new method for computing features of a drug-drug-cell line (DDC) combination based on similarity, where the S-kernel and Gaussian-kernel methods are used to calculate the drug-drug combination similarity and cell line similarity, respectively. The final feature vector for machine learning input was obtained by concatenating these two vectors. The output for machine learning was based on the experimental results of the synergistic drug combination. Cross validation was performed on three machine learning algorithms, including the random forest, support vector machine, and deep neural network models. The results suggested that the novel method had a robust performance with an area under the curve value of 0.89–0.91. Importantly, the model predicted the novel DDC combinations with new drugs or new cell lines based on unique input features. In conclusion, this novel method improved predictive performance and provided a new strategy for predicting synergistic drug combinations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1360/ssv-2023-0033
- https://www.sciengine.com/doi/pdfView/5BA869210A7D48F398374044CC9E7826
- OA Status
- bronze
- Cited By
- 1
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381387065
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381387065Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1360/ssv-2023-0033Digital Object Identifier
- Title
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Kernel-based prediction of a synergistic drug combinationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-20Full publication date if available
- Authors
-
Jun ZHANG, Rui Yuan, Shilong Chen, Yongcui WangList of authors in order
- Landing page
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https://doi.org/10.1360/ssv-2023-0033Publisher landing page
- PDF URL
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https://www.sciengine.com/doi/pdfView/5BA869210A7D48F398374044CC9E7826Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
bronzeOpen access status per OpenAlex
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https://www.sciengine.com/doi/pdfView/5BA869210A7D48F398374044CC9E7826Direct OA link when available
- Concepts
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Kernel (algebra), Drug, Computer science, Artificial intelligence, Mathematics, Machine learning, Medicine, Pharmacology, Discrete mathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.resistance. | 30 |
| abstract_inverted_index.similarity, | 66, 84 |
| abstract_inverted_index.synergistic | 114, 193 |
| abstract_inverted_index.0.89–0.91. | 157 |
| abstract_inverted_index.Importantly, | 158 |
| abstract_inverted_index.alternative. | 47 |
| abstract_inverted_index.combination. | 116 |
| abstract_inverted_index.combinations | 165 |
| abstract_inverted_index.considerable | 35 |
| abstract_inverted_index.experimental | 32, 110 |
| abstract_inverted_index.combinations. | 195 |
| abstract_inverted_index.computational | 42 |
| abstract_inverted_index.concatenating | 97 |
| abstract_inverted_index.heterogeneity | 16 |
| abstract_inverted_index.respectively. | 85 |
| abstract_inverted_index.drug-drug-cell | 60 |
| abstract_inverted_index.Gaussian-kernel | 71 |
| abstract_inverted_index.indent="0mm">Complex | 1 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.55510178 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |