Optimization of Cdx Transcription Factors Characteristics | Journal of Engineering Sciences

Optimization of Cdx Transcription Factors Characteristics

Author(s): Javanbakht T.

Affiliation(s): Department of Computer Science, University of Quebec in Montreal, 201, President Kennedy St., (QC) H2X 3Y7 Montreal, Canada

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 2 (2023)

Dates:
Submitted: April 19, 2023
Received in revised form: June 16, 2023
Accepted for publication: August 13, 2023
Available online: August 16, 2023

Citation:
Javanbakht T. (2023). Optimization of Cdx transcription factors characteristics. Journal of Engineering Sciences (Ukraine), Vol. 10(2), pp. E1–E7. DOI: 10.21272/jes.2023.10(2).e1

DOI: 10.21272/jes.2023.10(2).e1

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. This study presents a new application of TOPSIS for the optimization of transcription factors characteristics. This application is essential as it can help compare the characteristics of these proteins and determine the optimized output of their comparison with this decision-making method. The hypothesis in this article was that according to the previous study of the Cdx transcription factors, as the Cdx2 transcription factor showed more robust characteristics than Cdx1 and Cdx4, the TOPSIS method would show a better rank position of these first proteins in comparison with the two other ones. Moreover, the engrailed repressor domain EnRCdx1 used in the plasmid showed the reduction of the pax3 gene expression in comparison with the induced regulation of the gene expression with the production of the Cdx1, Cdx2, and Cdx4 transcription factors using the corresponding plasmids, the worst rank position with TOPSIS was expected for this repressor domain. The results obtained with this ranking method showed that the rank positions of the transcription factors and the repressor domain corresponded to their compared properties. Moreover, the change in the weight values of the candidates showed the modification of their distances from the best and worst alternatives and closeness coefficients. However, as expected, the candidates’ rank positions were unchanged, and the Cdx2 transcription factor was still the best candidate. The results of this article can be used in computer engineering to improve biological applications of these proteins.

Keywords: decision-making process, TOPSIS, algorithm, transcription factor, optimization.

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