Evaluating the analytical distribution of bicoid gene expression profile
Authors: Hassani, H. and Ghodsi, Z.
Journal: Meta Gene
Volume: 14
Pages: 91-99
eISSN: 2214-5400
DOI: 10.1016/j.mgene.2017.07.014
Abstract:Segmentation in Drosophila melanogaster starts with a key maternal input known as bicoid gene. The initial positional information provided by this gene induces the sequential activation of segmentation network. Therefore, an accurate mathematical model describing the gene expression profile of bicoid gene expects to provide essential insights into the gene cross-regulations presented in that network. The significantly stochastic, highly volatile and non-normal nature of the bicoid gene expression profile encouraged us to look for the best distribution function describing this profile. We exploit the use of fifty-four different powerful and widely-used distributions and conclude that FatigueLife(3P) fits the data more accurately than the other distributions. The reliability and validity of the results are evaluated via both simulation studies and empirical evidence thereby adding more confidence and value to the findings of this research.
https://eprints.bournemouth.ac.uk/29704/
Source: Scopus
Evaluating the analytical distribution of bicoid gene expression profile
Authors: Hassani, H. and Ghodsi, Z.
Journal: META GENE
Volume: 14
Pages: 91-99
ISSN: 2214-5400
DOI: 10.1016/j.mgene.2017.07.014
https://eprints.bournemouth.ac.uk/29704/
Source: Web of Science (Lite)
Evaluating the Analytical Distribution of bicoid Gene Expression Profile
Authors: Ghodsi, Z. and Hassani, H.
Journal: Meta Gene
https://eprints.bournemouth.ac.uk/29704/
Source: Manual
Evaluating the analytical distribution of bicoid gene expression profile
Authors: Hassani, H. and Ghodsi, Z.
Journal: Meta Gene
Volume: 14
Issue: December
Pages: 91-99
ISSN: 2214-5400
Abstract:Segmentation in Drosophila melanogaster starts with a key maternal input known as bicoid gene. The initial positional information provided by this gene induces the sequential activation of segmentation network.
Therefore, an accurate mathematical model describing the gene expression profile of bicoid gene expects to provide essential insights into the gene cross-regulations presented in that network. The significantly stochastic, highly volatile and non-normal nature of the bicoid gene expression profile encouraged us to look for the best distribution function describing this profile. We exploit the use of fifty-four different powerful and widely-used distributions and conclude that FatigueLife(3P) fits the data more accurately than the other distributions. The reliability and validity of the results are evaluated via both simulation studies and empirical evidence thereby adding more confidence and value to the findings of this research
https://eprints.bournemouth.ac.uk/29704/
Source: BURO EPrints