Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions

Network models of gene interactions, using time course gene transcript abundance data, are computationally created using a genetic algorithm designed to incorporate hierarchical Bayesian methods with time series adjustments. The posterior probabilities of interaction between pairs of genes are based...

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Autores principales: Allen, Edward E., Farrell, John, Harkey, Alexandria F., John, David J., Muday, Gloria, Norris, James L., Wu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197098/
http://dx.doi.org/10.1007/978-3-030-42266-0_11
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author Allen, Edward E.
Farrell, John
Harkey, Alexandria F.
John, David J.
Muday, Gloria
Norris, James L.
Wu, Bo
author_facet Allen, Edward E.
Farrell, John
Harkey, Alexandria F.
John, David J.
Muday, Gloria
Norris, James L.
Wu, Bo
author_sort Allen, Edward E.
collection PubMed
description Network models of gene interactions, using time course gene transcript abundance data, are computationally created using a genetic algorithm designed to incorporate hierarchical Bayesian methods with time series adjustments. The posterior probabilities of interaction between pairs of genes are based on likelihoods of directed acyclic graphs. This algorithm is applied to transcript abundance data collected from Arabidopsis thaliana genes. This study extends the underlying statistical and mathematical theory of the Norris-Patton likelihood by including time series adjustments.
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spelling pubmed-71970982020-05-04 Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions Allen, Edward E. Farrell, John Harkey, Alexandria F. John, David J. Muday, Gloria Norris, James L. Wu, Bo Algorithms for Computational Biology Article Network models of gene interactions, using time course gene transcript abundance data, are computationally created using a genetic algorithm designed to incorporate hierarchical Bayesian methods with time series adjustments. The posterior probabilities of interaction between pairs of genes are based on likelihoods of directed acyclic graphs. This algorithm is applied to transcript abundance data collected from Arabidopsis thaliana genes. This study extends the underlying statistical and mathematical theory of the Norris-Patton likelihood by including time series adjustments. 2020-02-01 /pmc/articles/PMC7197098/ http://dx.doi.org/10.1007/978-3-030-42266-0_11 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Allen, Edward E.
Farrell, John
Harkey, Alexandria F.
John, David J.
Muday, Gloria
Norris, James L.
Wu, Bo
Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions
title Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions
title_full Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions
title_fullStr Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions
title_full_unstemmed Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions
title_short Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions
title_sort time series adjustment enhancement of hierarchical modeling of arabidopsis thaliana gene interactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197098/
http://dx.doi.org/10.1007/978-3-030-42266-0_11
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