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SiGMoiD: A super-statistical generative model for binary data
In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372922/ https://www.ncbi.nlm.nih.gov/pubmed/34358223 http://dx.doi.org/10.1371/journal.pcbi.1009275 |
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author | Zhao, Xiaochuan Plata, Germán Dixit, Purushottam D. |
author_facet | Zhao, Xiaochuan Plata, Germán Dixit, Purushottam D. |
author_sort | Zhao, Xiaochuan |
collection | PubMed |
description | In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows us to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using multiple datasets spanning several time- and length-scales. |
format | Online Article Text |
id | pubmed-8372922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83729222021-08-19 SiGMoiD: A super-statistical generative model for binary data Zhao, Xiaochuan Plata, Germán Dixit, Purushottam D. PLoS Comput Biol Research Article In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows us to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using multiple datasets spanning several time- and length-scales. Public Library of Science 2021-08-06 /pmc/articles/PMC8372922/ /pubmed/34358223 http://dx.doi.org/10.1371/journal.pcbi.1009275 Text en © 2021 Zhao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhao, Xiaochuan Plata, Germán Dixit, Purushottam D. SiGMoiD: A super-statistical generative model for binary data |
title | SiGMoiD: A super-statistical generative model for binary data |
title_full | SiGMoiD: A super-statistical generative model for binary data |
title_fullStr | SiGMoiD: A super-statistical generative model for binary data |
title_full_unstemmed | SiGMoiD: A super-statistical generative model for binary data |
title_short | SiGMoiD: A super-statistical generative model for binary data |
title_sort | sigmoid: a super-statistical generative model for binary data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372922/ https://www.ncbi.nlm.nih.gov/pubmed/34358223 http://dx.doi.org/10.1371/journal.pcbi.1009275 |
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