Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo
Authors: Ghodsi, Z., Huang, X. and Hassani, H.
Journal: Genomics Data
Volume: 11
Pages: 20-38
ISSN: 2213-5960
DOI: 10.1016/j.gdata.2016.11.013
Abstract:In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.
https://eprints.bournemouth.ac.uk/29717/
Source: Scopus
Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo.
Authors: Ghodsi, Z., Huang, X. and Hassani, H.
Journal: Genom Data
Volume: 11
Pages: 20-38
ISSN: 2213-5960
DOI: 10.1016/j.gdata.2016.11.013
Abstract:In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.
https://eprints.bournemouth.ac.uk/29717/
Source: PubMed
Causality analysis detects the regulatory role of maternal effect genes in the early <i>Drosophila</i> embryo
Authors: Ghodsi, Z., Huang, X. and Hassani, H.
Journal: GENOMICS DATA
Volume: 11
Pages: 20-38
ISSN: 2213-5960
DOI: 10.1016/j.gdata.2016.11.013
https://eprints.bournemouth.ac.uk/29717/
Source: Web of Science (Lite)
Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo
Authors: Ghodsi, Z., Huang, X. and Hassani, H.
Journal: GENOMICS DATA
Volume: 11
Pages: 20-38
ISSN: 2213-5960
DOI: 10.1016/j.gdata.2016.11.013
https://eprints.bournemouth.ac.uk/29717/
Source: Manual
Causality analysis detects the regulatory role of maternal effect genes in the early <i>Drosophila</i> embryo.
Authors: Ghodsi, Z., Huang, X. and Hassani, H.
Journal: Genomics data
Volume: 11
Pages: 20-38
eISSN: 2213-5960
ISSN: 2213-5960
DOI: 10.1016/j.gdata.2016.11.013
Abstract:In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.
https://eprints.bournemouth.ac.uk/29717/
Source: Europe PubMed Central
Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo
Authors: Ghodsi, Z., Huang, X. and Hassani, H.
Journal: Genomics Data
Volume: 11
Issue: March
Pages: 20-38
ISSN: 2213-5960
Abstract:In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.
https://eprints.bournemouth.ac.uk/29717/
Source: BURO EPrints