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Classification of the Senescence-Accelerated Mouse (SAM) Strains With Its Behaviour Using Deep Learning

Classification of the Senescence-Accelerated Mouse (SAM) Strains With Its Behaviour Using Deep Learning

Sura Zaki AlRashid, Mohammed Hussein Dosh, Ahmed J. Obaid
Copyright: © 2022 |Volume: 18 |Issue: 2 |Pages: 13
ISSN: 1548-3673|EISSN: 1548-3681|EISBN13: 9781799893875|DOI: 10.4018/IJeC.304035
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MLA

AlRashid, Sura Zaki, et al. "Classification of the Senescence-Accelerated Mouse (SAM) Strains With Its Behaviour Using Deep Learning." IJEC vol.18, no.2 2022: pp.1-13. http://doi.org/10.4018/IJeC.304035

APA

AlRashid, S. Z., Dosh, M. H., & Obaid, A. J. (2022). Classification of the Senescence-Accelerated Mouse (SAM) Strains With Its Behaviour Using Deep Learning. International Journal of e-Collaboration (IJeC), 18(2), 1-13. http://doi.org/10.4018/IJeC.304035

Chicago

AlRashid, Sura Zaki, Mohammed Hussein Dosh, and Ahmed J. Obaid. "Classification of the Senescence-Accelerated Mouse (SAM) Strains With Its Behaviour Using Deep Learning," International Journal of e-Collaboration (IJeC) 18, no.2: 1-13. http://doi.org/10.4018/IJeC.304035

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Abstract

Microarray technology is a novel method to monitor the levels of expression of a huge number of genes simultaneously.this study aims at (1) identifying the most important genes in the molecular senescence of the hippocampus and retina, where both with accelerated neurological senescence (S10 and 8) models were obtainable. By using feature selection to reduce the size of high dimensional data. Hence, the process of gene selection is twofold; removing the irrelevant genes and selecting the informative genes, and (2) The determination of the study is to specify the association among these genes or pathways that would deliver insight into the mechanism for this phenotype which will be greater to the current imperfect state-of-the-art estimates. In this study, gene selection methods have been implemented, including Analysis of Variance (ANOVA). The results are showed that CNN model achieve 0.98 accuracy based on a subset of genes from ANOVA method. Thus, Genes subset selected is achieved a better accuracy at classification and a little time of processing.

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