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Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference

Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathe...

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Autores principales: Amodio, Matthew, Shung, Dennis, Burkhardt, Daniel B., Wong, Patrick, Simonov, Michael, Yamamoto, Yu, van Dijk, David, Wilson, Francis Perry, Iwasaki, Akiko, Krishnaswamy, Smita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276014/
https://www.ncbi.nlm.nih.gov/pubmed/34286302
http://dx.doi.org/10.1016/j.patter.2021.100288
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author Amodio, Matthew
Shung, Dennis
Burkhardt, Daniel B.
Wong, Patrick
Simonov, Michael
Yamamoto, Yu
van Dijk, David
Wilson, Francis Perry
Iwasaki, Akiko
Krishnaswamy, Smita
author_facet Amodio, Matthew
Shung, Dennis
Burkhardt, Daniel B.
Wong, Patrick
Simonov, Michael
Yamamoto, Yu
van Dijk, David
Wilson, Francis Perry
Iwasaki, Akiko
Krishnaswamy, Smita
author_sort Amodio, Matthew
collection PubMed
description Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some. We propose building a model to make probabilistic predictions of HI using EI. Our feature mapping GAN (FMGAN), based on the conditional GAN framework, uses an embedding network to process conditions as part of the conditional GAN training to create manifold structure when it is not readily present in the conditions. We experiment on generating RNA sequencing of cell lines perturbed with a drug conditioned on the drug's chemical structure and generating FACS data from clinical monitoring variables on a cohort of COVID-19 patients, effectively describing their immune response in great detail.
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spelling pubmed-82760142021-07-19 Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference Amodio, Matthew Shung, Dennis Burkhardt, Daniel B. Wong, Patrick Simonov, Michael Yamamoto, Yu van Dijk, David Wilson, Francis Perry Iwasaki, Akiko Krishnaswamy, Smita Patterns (N Y) Article Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some. We propose building a model to make probabilistic predictions of HI using EI. Our feature mapping GAN (FMGAN), based on the conditional GAN framework, uses an embedding network to process conditions as part of the conditional GAN training to create manifold structure when it is not readily present in the conditions. We experiment on generating RNA sequencing of cell lines perturbed with a drug conditioned on the drug's chemical structure and generating FACS data from clinical monitoring variables on a cohort of COVID-19 patients, effectively describing their immune response in great detail. Elsevier 2021-06-17 /pmc/articles/PMC8276014/ /pubmed/34286302 http://dx.doi.org/10.1016/j.patter.2021.100288 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Amodio, Matthew
Shung, Dennis
Burkhardt, Daniel B.
Wong, Patrick
Simonov, Michael
Yamamoto, Yu
van Dijk, David
Wilson, Francis Perry
Iwasaki, Akiko
Krishnaswamy, Smita
Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
title Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
title_full Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
title_fullStr Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
title_full_unstemmed Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
title_short Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
title_sort generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276014/
https://www.ncbi.nlm.nih.gov/pubmed/34286302
http://dx.doi.org/10.1016/j.patter.2021.100288
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