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Cell-specific imputation of drug connectivity mapping with incomplete data
Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. “Connectivity mapping” is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934325/ https://www.ncbi.nlm.nih.gov/pubmed/36795645 http://dx.doi.org/10.1371/journal.pone.0278289 |
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author | Sapashnik, Diana Newman, Rebecca Pietras, Christopher Michael Zhou, Di Devkota, Kapil Qu, Fangfang Kofman, Lior Boudreau, Sean Fried, Inbar Slonim, Donna K. |
author_facet | Sapashnik, Diana Newman, Rebecca Pietras, Christopher Michael Zhou, Di Devkota, Kapil Qu, Fangfang Kofman, Lior Boudreau, Sean Fried, Inbar Slonim, Donna K. |
author_sort | Sapashnik, Diana |
collection | PubMed |
description | Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. “Connectivity mapping” is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease’s impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing. To evaluate the possibility of repurposing drugs despite missing data, we compared collaborative filtering using either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. Methods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease. |
format | Online Article Text |
id | pubmed-9934325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99343252023-02-17 Cell-specific imputation of drug connectivity mapping with incomplete data Sapashnik, Diana Newman, Rebecca Pietras, Christopher Michael Zhou, Di Devkota, Kapil Qu, Fangfang Kofman, Lior Boudreau, Sean Fried, Inbar Slonim, Donna K. PLoS One Research Article Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. “Connectivity mapping” is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease’s impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing. To evaluate the possibility of repurposing drugs despite missing data, we compared collaborative filtering using either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. Methods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease. Public Library of Science 2023-02-16 /pmc/articles/PMC9934325/ /pubmed/36795645 http://dx.doi.org/10.1371/journal.pone.0278289 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Sapashnik, Diana Newman, Rebecca Pietras, Christopher Michael Zhou, Di Devkota, Kapil Qu, Fangfang Kofman, Lior Boudreau, Sean Fried, Inbar Slonim, Donna K. Cell-specific imputation of drug connectivity mapping with incomplete data |
title | Cell-specific imputation of drug connectivity mapping with incomplete data |
title_full | Cell-specific imputation of drug connectivity mapping with incomplete data |
title_fullStr | Cell-specific imputation of drug connectivity mapping with incomplete data |
title_full_unstemmed | Cell-specific imputation of drug connectivity mapping with incomplete data |
title_short | Cell-specific imputation of drug connectivity mapping with incomplete data |
title_sort | cell-specific imputation of drug connectivity mapping with incomplete data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934325/ https://www.ncbi.nlm.nih.gov/pubmed/36795645 http://dx.doi.org/10.1371/journal.pone.0278289 |
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