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
![]() |
PDF
Restricted to Repository staff only Download (300kB) | Request a copy |
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: | Mr. 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 |
Actions (login required)
![]() |
View Item |