Comparative analysis of mice protein expression clustering and classification approach
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 learn...
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| Format: | Article |
| Language: | English |
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Penerbit UTHM
2018
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| Online Access: | http://eprints.uthm.edu.my/4502/ http://eprints.uthm.edu.my/4502/1/AJ%202018%20%28779%29%20Comparative%20analysis%20of%20mice%20protein%20expression%20clustering%20and%20classification%20approach.pdf |
| _version_ | 1848888305546952704 |
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| author | Saringat, Mohd Zainuri Mustapha, Aida Andeswari, Rachmadita |
| author_facet | Saringat, Mohd Zainuri Mustapha, Aida Andeswari, Rachmadita |
| author_sort | Saringat, Mohd Zainuri |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T20:08:10Z |
| format | Article |
| id | uthm-4502 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:08:10Z |
| publishDate | 2018 |
| publisher | Penerbit UTHM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-45022021-12-07T04:25:57Z http://eprints.uthm.edu.my/4502/ Comparative analysis of mice protein expression clustering and classification approach Saringat, Mohd Zainuri Mustapha, Aida Andeswari, Rachmadita QA299.6-433 Analysis TA329-348 Engineering mathematics. Engineering analysis 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. Penerbit UTHM 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4502/1/AJ%202018%20%28779%29%20Comparative%20analysis%20of%20mice%20protein%20expression%20clustering%20and%20classification%20approach.pdf 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 |
| spellingShingle | QA299.6-433 Analysis TA329-348 Engineering mathematics. Engineering analysis Saringat, Mohd Zainuri Mustapha, Aida Andeswari, Rachmadita Comparative analysis of mice protein expression clustering and classification approach |
| title | Comparative analysis of mice protein expression clustering and classification approach |
| title_full | Comparative analysis of mice protein expression clustering and classification approach |
| title_fullStr | Comparative analysis of mice protein expression clustering and classification approach |
| title_full_unstemmed | Comparative analysis of mice protein expression clustering and classification approach |
| title_short | Comparative analysis of mice protein expression clustering and classification approach |
| title_sort | comparative analysis of mice protein expression clustering and classification approach |
| topic | QA299.6-433 Analysis TA329-348 Engineering mathematics. Engineering analysis |
| url | http://eprints.uthm.edu.my/4502/ http://eprints.uthm.edu.my/4502/1/AJ%202018%20%28779%29%20Comparative%20analysis%20of%20mice%20protein%20expression%20clustering%20and%20classification%20approach.pdf |