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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...

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Autores principales: Sapashnik, Diana, Newman, Rebecca, Pietras, Christopher Michael, Zhou, Di, Devkota, Kapil, Qu, Fangfang, Kofman, Lior, Boudreau, Sean, Fried, Inbar, Slonim, Donna K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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