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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-8276014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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