Molecular data representation based on gene embeddings for cancer drug response prediction Article Swipe
Se Jin Park
,
Hyunju Lee
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-49003-6
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-49003-6
Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-023-49003-6
- https://www.nature.com/articles/s41598-023-49003-6.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389570579
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389570579Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-023-49003-6Digital Object Identifier
- Title
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Molecular data representation based on gene embeddings for cancer drug response predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-11Full publication date if available
- Authors
-
Se Jin Park, Hyunju LeeList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-023-49003-6Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-023-49003-6.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-023-49003-6.pdfDirect OA link when available
- Concepts
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Representation (politics), Drug response, Computational biology, Drug, Computer science, Gene, Cancer, External Data Representation, Bioinformatics, Data mining, Biology, Genetics, Artificial intelligence, Pharmacology, Law, Politics, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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