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Latent representation learning in biology and translational medicine
Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable m...
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/PMC7961186/ https://www.ncbi.nlm.nih.gov/pubmed/33748792 http://dx.doi.org/10.1016/j.patter.2021.100198 |
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author | Kopf, Andreas Claassen, Manfred |
author_facet | Kopf, Andreas Claassen, Manfred |
author_sort | Kopf, Andreas |
collection | PubMed |
description | Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling allows for such interpretation by learning non-measurable hidden variables from observations. This review gives an overview over the different formal approaches to latent variable modeling, as well as applications at different scales of biological systems, such as molecular structures, intra- and intercellular regulatory up to physiological networks. The focus is on demonstrating how these approaches have enabled interpretable representations and ultimately insights in each of these domains. We anticipate that a wider dissemination of latent variable modeling in the life sciences will enable a more effective and productive interpretation of studies based on heterogeneous and high-dimensional data modalities. |
format | Online Article Text |
id | pubmed-7961186 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-79611862021-03-19 Latent representation learning in biology and translational medicine Kopf, Andreas Claassen, Manfred Patterns (N Y) Review Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling allows for such interpretation by learning non-measurable hidden variables from observations. This review gives an overview over the different formal approaches to latent variable modeling, as well as applications at different scales of biological systems, such as molecular structures, intra- and intercellular regulatory up to physiological networks. The focus is on demonstrating how these approaches have enabled interpretable representations and ultimately insights in each of these domains. We anticipate that a wider dissemination of latent variable modeling in the life sciences will enable a more effective and productive interpretation of studies based on heterogeneous and high-dimensional data modalities. Elsevier 2021-03-12 /pmc/articles/PMC7961186/ /pubmed/33748792 http://dx.doi.org/10.1016/j.patter.2021.100198 Text en © 2021 The Authors http://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 | Review Kopf, Andreas Claassen, Manfred Latent representation learning in biology and translational medicine |
title | Latent representation learning in biology and translational medicine |
title_full | Latent representation learning in biology and translational medicine |
title_fullStr | Latent representation learning in biology and translational medicine |
title_full_unstemmed | Latent representation learning in biology and translational medicine |
title_short | Latent representation learning in biology and translational medicine |
title_sort | latent representation learning in biology and translational medicine |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961186/ https://www.ncbi.nlm.nih.gov/pubmed/33748792 http://dx.doi.org/10.1016/j.patter.2021.100198 |
work_keys_str_mv | AT kopfandreas latentrepresentationlearninginbiologyandtranslationalmedicine AT claassenmanfred latentrepresentationlearninginbiologyandtranslationalmedicine |