A Mouse-Specific Model to Detect Genes under Selection in Tumors Article Swipe
Hai Chen
,
Jingmin Shu
,
Carlo C. Maley
,
Li Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/cancers15215156
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/cancers15215156
The mouse is a widely used model organism in cancer research. However, no computational methods exist to identify cancer driver genes in mice due to a lack of labeled training data. To address this knowledge gap, we adapted the GUST (Genes Under Selection in Tumors) model, originally trained on human exomes, to mouse exomes via transfer learning. The resulting tool, called GUST-mouse, can estimate long-term and short-term evolutionary selection in mouse tumors, and distinguish between oncogenes, tumor suppressor genes, and passenger genes using high-throughput sequencing data. We applied GUST-mouse to analyze 65 exomes of mouse primary breast cancer models and 17 exomes of mouse leukemia models. Comparing the predictions between cancer types and between human and mouse tumors revealed common and unique driver genes. The GUST-mouse method is available as an open-source R package on github.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/cancers15215156
- https://www.mdpi.com/2072-6694/15/21/5156/pdf?version=1698313812
- OA Status
- gold
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387957400
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387957400Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/cancers15215156Digital Object Identifier
- Title
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A Mouse-Specific Model to Detect Genes under Selection in TumorsWork 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
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2023-10-26Full publication date if available
- Authors
-
Hai Chen, Jingmin Shu, Carlo C. Maley, Li LiuList of authors in order
- Landing page
-
https://doi.org/10.3390/cancers15215156Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-6694/15/21/5156/pdf?version=1698313812Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-6694/15/21/5156/pdf?version=1698313812Direct OA link when available
- Concepts
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Exome sequencing, Gene, Biology, Computational biology, Selection (genetic algorithm), Suppressor, Cancer, Genetics, Phenotype, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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57Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | True |
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| publication_year | 2023 |
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| abstract_inverted_index.a | 3, 25 |
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| abstract_inverted_index.65 | 91 |
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| abstract_inverted_index.We | 86 |
| abstract_inverted_index.an | 130 |
| abstract_inverted_index.as | 129 |
| abstract_inverted_index.in | 8, 21, 43, 69 |
| abstract_inverted_index.is | 2, 127 |
| abstract_inverted_index.no | 12 |
| abstract_inverted_index.of | 27, 93, 102 |
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| abstract_inverted_index.to | 16, 24, 51, 89 |
| abstract_inverted_index.we | 36 |
| abstract_inverted_index.The | 0, 57, 124 |
| abstract_inverted_index.and | 65, 72, 79, 99, 112, 115, 120 |
| abstract_inverted_index.can | 62 |
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| abstract_inverted_index.the | 38, 107 |
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| abstract_inverted_index.tool, | 59 |
| abstract_inverted_index.tumor | 76 |
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| abstract_inverted_index.driver | 19, 122 |
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| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I55732556 |
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| citation_normalized_percentile.is_in_top_10_percent | False |