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

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

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Abstract

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.

Item Type: Article (Journal)
Additional Information: 6919/60441
Uncontrolled Keywords: Adjacent normal tissue; cancer classification; PCA; RLS
Subjects: A General Works > AI Indexes (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr. Zaw Zaw Htike
Date Deposited: 24 Dec 2017 22:39
Last Modified: 11 Jul 2018 09:15
URI: http://irep.iium.edu.my/id/eprint/60441

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