Comparative analysis of mice protein expression clustering and classification approach

Saringat, Mohd Zainuri and Mustapha, Aida and Andeswari, Rachmadita (2018) Comparative analysis of mice protein expression clustering and classification approach. International Journal of Integrated Engineering, 10 (6). pp. 26-30. ISSN 2229-838X

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Abstract

The mice protein expression dataset was created to study the effect of learning between normal and trisomic mice or mice with Down Syndrome (DS). The extra copy of a normal chromosome in DS is believed to be the cause that alters the normal pathways and normal responses to stimulation, causing learning and memory deficits. This research attempts to analyze the protein expression dataset on protein influences that could have affected the recovering ability to learn among the trisomic mice. Two data mining tasks are employed; clustering and classification analysis. Clustering analysis via K-Means, Hierarchical Clustering, and Decision Tree have been proven useful to identify common critical protein responses, which in turn helping in identifying potentially more effective drug targets. Meanwhile, all classification models including the k-Nearest Neighbor, Random Forest, and Naive Bayes have efficiently classifies protein samples into the given eight classes with very high accuracy.

Item Type:	Article
Uncontrolled Keywords:	Classification; clustering; medical data mining.
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis T Technology > TA Engineering (General). Civil engineering (General) > TA329-348 Engineering mathematics. Engineering analysis
Divisions:	Faculty of Applied Science and Technology > Department of Mathematics and Statistics
Depositing User:	UiTM Student Praktikal
Date Deposited:	07 Dec 2021 04:25
Last Modified:	07 Dec 2021 04:25
URI:	http://eprints.uthm.edu.my/id/eprint/4502

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