Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer
This study applies machine learning methods to gene expression data from normal tissue of patients with liver cancer to predict whether this tissue is 'healthy', 'cirrhotic' (liver damage), 'non tumor', or 'tumor'. The method is based on using Principle Compon...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Research India Publications
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/60441/ http://irep.iium.edu.my/60441/1/78_60552-IJAER%20ok%2012319-12321.pdf |
| _version_ | 1848785493118943232 |
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| author | Shams, Wafaa Kazaal Htike@Muhammad Yusof, Zaw Zaw |
| author_facet | Shams, Wafaa Kazaal Htike@Muhammad Yusof, Zaw Zaw |
| author_sort | Shams, Wafaa Kazaal |
| building | IIUM Repository |
| collection | Online Access |
| description | This study applies machine learning methods to gene expression data from normal tissue of patients with liver cancer to predict whether this tissue is 'healthy', 'cirrhotic' (liver damage), 'non tumor', or 'tumor'. The method is based on using Principle Component Analysis (PCA) combined with the Regularized Least Squares (RLS) classifier. The results show a high accuracy with 10-fold cross validation for discrimination among tissue types. Results indicate the capability of gene expression profiling to successfully discriminate between tumor tissue and normal tissue, however there is a clear and strong overlap between non-tumor tissue and cirrhotic tissue. Further, we used the same classification model to predicate the probability of detecting each class separately. Tumor gene expression can be predicated successfully. |
| first_indexed | 2025-11-14T16:54:01Z |
| format | Article |
| id | iium-60441 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:54:01Z |
| publishDate | 2017 |
| publisher | Research India Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-604412018-07-11T01:15:28Z http://irep.iium.edu.my/60441/ Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer Shams, Wafaa Kazaal Htike@Muhammad Yusof, Zaw Zaw AI Indexes (General) This study applies machine learning methods to gene expression data from normal tissue of patients with liver cancer to predict whether this tissue is 'healthy', 'cirrhotic' (liver damage), 'non tumor', or 'tumor'. The method is based on using Principle Component Analysis (PCA) combined with the Regularized Least Squares (RLS) classifier. The results show a high accuracy with 10-fold cross validation for discrimination among tissue types. Results indicate the capability of gene expression profiling to successfully discriminate between tumor tissue and normal tissue, however there is a clear and strong overlap between non-tumor tissue and cirrhotic tissue. Further, we used the same classification model to predicate the probability of detecting each class separately. Tumor gene expression can be predicated successfully. Research India Publications 2017-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/60441/1/78_60552-IJAER%20ok%2012319-12321.pdf Shams, Wafaa Kazaal and Htike@Muhammad Yusof, Zaw Zaw (2017) Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer. International Journal of Applied Engineering Research, 12 (22). pp. 12319-12321. ISSN 0973-4562 http://www.ripublication.com |
| spellingShingle | AI Indexes (General) Shams, Wafaa Kazaal Htike@Muhammad Yusof, Zaw Zaw Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| title | Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| title_full | Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| title_fullStr | Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| title_full_unstemmed | Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| title_short | Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| title_sort | application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer |
| topic | AI Indexes (General) |
| url | http://irep.iium.edu.my/60441/ http://irep.iium.edu.my/60441/ http://irep.iium.edu.my/60441/1/78_60552-IJAER%20ok%2012319-12321.pdf |