Drug target prediction through deep learning functional representation of gene signatures Article Swipe
Yingyao Zhou
,
Hao Chen
,
Frederick J. King
,
Bin Zhou
,
Yu Wang
,
Carter Canedy
,
Joel Hayashi
,
Yong Zhong
,
Max W. Chang
,
Lars Pache
,
Julian Wong
,
Yong Jia
,
John M. Joslin
,
Tao Jiang
,
Christopher Benner
,
Sumit K. Chanda
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3371688/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3371688/v1
Many machine learning applications in bioinformatics currently rely on gene identities extracted from input gene signatures, and fail to take advantage of preexisting knowledge about gene functions. We developed the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model. FRoGS represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We demonstrated that its application to L1000 datasets resulted in more effective compound-target predictions than models based on gene identities alone. Through further integration of additional pharmacological activity data sources, FRoGS significantly contributed to a large number of high-quality compound-target predictions, which were supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help more readily uncover relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3371688/v1
- https://www.researchsquare.com/article/rs-3371688/latest.pdf
- OA Status
- green
- Cited By
- 4
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387139392
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387139392Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-3371688/v1Digital Object Identifier
- Title
-
Drug target prediction through deep learning functional representation of gene signaturesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-28Full publication date if available
- Authors
-
Yingyao Zhou, Hao Chen, Frederick J. King, Bin Zhou, Yu Wang, Carter Canedy, Joel Hayashi, Yong Zhong, Max W. Chang, Lars Pache, Julian Wong, Yong Jia, John M. Joslin, Tao Jiang, Christopher Benner, Sumit K. ChandaList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3371688/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3371688/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-3371688/latest.pdfDirect OA link when available
- Concepts
-
Drug, Representation (politics), Computational biology, Drug target, Artificial intelligence, Computer science, Gene, Machine learning, Biology, Genetics, Pharmacology, Political science, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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77Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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