A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis
The Multi-Encoder Variational AutoEncoder (ME-VAE) is a computational model that can control for multiple transformational features in single-cell imaging data, enabling researchers to extract meaningful single-cell information and better separate heterogeneous cell types.
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-03-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-022-03218-x |
| _version_ | 1818776080644833280 |
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| author | Luke Ternes Mark Dane Sean Gross Marilyne Labrie Gordon Mills Joe Gray Laura Heiser Young Hwan Chang |
| author_facet | Luke Ternes Mark Dane Sean Gross Marilyne Labrie Gordon Mills Joe Gray Laura Heiser Young Hwan Chang |
| author_sort | Luke Ternes |
| collection | DOAJ |
| description | The Multi-Encoder Variational AutoEncoder (ME-VAE) is a computational model that can control for multiple transformational features in single-cell imaging data, enabling researchers to extract meaningful single-cell information and better separate heterogeneous cell types. |
| first_indexed | 2024-12-18T11:07:15Z |
| format | Article |
| id | doaj.art-62591d36456a47f795a757db9843ff3a |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-12-18T11:07:15Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-62591d36456a47f795a757db9843ff3a2022-12-21T21:10:04ZengNature PortfolioCommunications Biology2399-36422022-03-015111010.1038/s42003-022-03218-xA multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysisLuke Ternes0Mark Dane1Sean Gross2Marilyne Labrie3Gordon Mills4Joe Gray5Laura Heiser6Young Hwan Chang7Biomedical Engineering Department, Oregon Health & Science UniversityBiomedical Engineering Department, Oregon Health & Science UniversityBiomedical Engineering Department, Oregon Health & Science UniversityCell, Developmental and Cancer Biology Department, Oregon Health & Science UniversityCell, Developmental and Cancer Biology Department, Oregon Health & Science UniversityBiomedical Engineering Department, Oregon Health & Science UniversityBiomedical Engineering Department, Oregon Health & Science UniversityBiomedical Engineering Department, Oregon Health & Science UniversityThe Multi-Encoder Variational AutoEncoder (ME-VAE) is a computational model that can control for multiple transformational features in single-cell imaging data, enabling researchers to extract meaningful single-cell information and better separate heterogeneous cell types.https://doi.org/10.1038/s42003-022-03218-x |
| spellingShingle | Luke Ternes Mark Dane Sean Gross Marilyne Labrie Gordon Mills Joe Gray Laura Heiser Young Hwan Chang A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis Communications Biology |
| title | A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis |
| title_full | A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis |
| title_fullStr | A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis |
| title_full_unstemmed | A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis |
| title_short | A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis |
| title_sort | multi encoder variational autoencoder controls multiple transformational features in single cell image analysis |
| url | https://doi.org/10.1038/s42003-022-03218-x |
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