Prediction of Gene Expression Patterns With Generalized Linear Regression Model
Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding. Oct4, a core pluripotency factor, has especially played a key role in somatic cell reprogramming through transcriptional c...
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Published in | Frontiers in genetics Vol. 10; p. 120 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
04.03.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding. Oct4, a core pluripotency factor, has especially played a key role in somatic cell reprogramming through transcriptional control and affects the expression level of genes by its combination intensity. However, the quantitative relationship between Oct4 combination intensity and target gene expression is still not clear. Therefore, firstly, a generalized linear regression method was constructed to predict gene expression values in promoter regions affected by Oct4 combination intensity. Training data, including Oct4 combination intensity and target gene expression, were from promoter regions of genes with different cell development stages. Additionally, the quantitative relationship between gene expression and Oct4 combination intensity was analyzed with the proposed model. Then, the quantitative relationship between gene expression and Oct4 combination intensity at each stage of cell development was classified into high and low levels. Experimental analysis showed that the combination height of Oct4-inhibited gene expression decremented by a temporal exponential value, whereas the combination width of Oct4-promoted gene expression incremented by a temporal logarithmic value. Experimental results showed that the proposed method can achieve goodness of fit with high confidence. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics Edited by: Arun Kumar Sangaiah, VIT University, India Reviewed by: Yu-Dong Zhang, University of Leicester, United Kingdom; Jose Tenreiro Machado, Instituto Superior de Engenharia do Porto (ISEP), Portugal; Jianzhong Su, Wenzhou Medical University, China |
ISSN: | 1664-8021 1664-8021 |
DOI: | 10.3389/fgene.2019.00120 |