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...

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Main Authors:	Shams, Wafaa Kazaal, Htike@Muhammad Yusof, Zaw Zaw
Format:	Article
Language:	English
Published:	Research India Publications 2017
Subjects:	AI Indexes (General)
Online Access:	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

id	iium-60441
recordtype	eprints
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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	AI Indexes (General)
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
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.
format	Article
author	Shams, Wafaa Kazaal Htike@Muhammad Yusof, Zaw Zaw
author_facet	Shams, Wafaa Kazaal Htike@Muhammad Yusof, Zaw Zaw
author_sort	Shams, Wafaa Kazaal
title	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_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_sort	application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer
publisher	Research India Publications
publishDate	2017
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
first_indexed	2023-09-18T21:25:42Z
last_indexed	2023-09-18T21:25:42Z
_version_	1777412176246472704

Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer

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