Filtration of DNA Nucleotide Gene Expression Profiles in the Systems of Biological Objects Clustering

Babichev, Sergii; Taif, Mohamed Ali; Lytvynenko, Volodymyr

doi:10.18052/www.scipress.com/IFSL.8.1

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Filtration of DNA Nucleotide Gene Expression Profiles in the Systems of Biological Objects Clustering

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Abstract:

Researches on an optimization of the filtration process of DNA nucleotides gene expression profiles are presented in the article. The data of lung cancer patients E-GEOD-68571 of Array Express database were used as experimental data. Filtration was carried out under the terms of the expression detecting of corresponding gene, herewith the variance of gene expression, the absolute value of expression and the Shannon entropy were used as criteria. The value of thresholding coefficient was estimated on the basis of average (of) proximity measure of objects within the homogenous group and between groups. 470 columns were removed in the process of data filtering, and the matrix dimension of the test data has changed from (96´7129) to (96´6659). Estimation of the quality of information processing was performed by the comparative analysis of the clustering results of processed and unprocessed data.

Info:

Periodical:

International Frontier Science Letters (Volume 8)

Pages:

1-8

DOI:

https://doi.org/10.18052/www.scipress.com/IFSL.8.1

Citation:

S. Babichev et al., "Filtration of DNA Nucleotide Gene Expression Profiles in the Systems of Biological Objects Clustering", International Frontier Science Letters, Vol. 8, pp. 1-8, 2016

Online since:

June 2016

Authors:

Sergii Babichev, Mohamed Ali Taif, Volodymyr Lytvynenko

Keywords:

Clustering, DNA Nucleotide, Filtration, Gene Expression, Thresholding

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Distribution:

Open Access

This work is licensed under a
Creative Commons Attribution 4.0 International License

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Cited By:

[1] S. Babichev, V. Lytvynenko, M. Korobchynskyi, M. Taiff, Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation, Vol. 716, p. 359, 2017

DOI: https://doi.org/10.1007/978-3-319-58274-0_29

[2] S. Babichev, "Technology of Wavelet-Filtration of the Gene Expression Profiles in Order to Remove the Background Noise", Upravlâûŝie sistemy i mašiny, p. 25, 2017

DOI: https://doi.org/10.15407/usim.2017.05.025

[3] V. Osypenko, V. Osypenko, "Application of Inductive Modeling Principles to Solve the Double Clustering Problems", 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), p. 398, 2021

DOI: https://doi.org/10.1109/CSIT52700.2021.9648630