Imputing Missing Dna Microarray Data Using Nearest Neighbour (NN) In Least Square Data Imputation Algorithm For Survival Analysis

CHAM, WEI LUN (2010) Imputing Missing Dna Microarray Data Using Nearest Neighbour (NN) In Least Square Data Imputation Algorithm For Survival Analysis. Other thesis, Universiti Teknologi Malaysia.

[img]
Preview
PDF
chamweilunac06003910ttp.pdf

Download (550Kb) | Preview

Abstract

Survival analysis is the study to predict the patient’s times towards death, relapse or metastasis. For survival analysis, the quality of the gene input data is very important. The genes are obtained from DNA microarray. However, there exist problems as the data might be missing and it will affect the final result of the survival analysis. However, there exist methods to fill in the missing DNA microarray data. In this study, Nearest Neighbour in Least Square Data Imputation is an algorithm designed to fill in the missing DNA microarray data. This method is also widely known as the INI algorithm. In this study, the algorithm will be implemented. The datasets used are carcinoma and Diffuse Large B-Cell Lymphoma (DBLCL), which both of them are type of cancers. After imputation, there is a need to plot a Kaplan Meier survival analysis graph to get the final result for analysis. Then the comparison with other imputation methods will be conduct. Next will be the discussion of the contributions and also the limitations on this research. Lastly, the future works will also be concluded in this research.

Item Type: Thesis (Other)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science and Information System > Software Engineering
Depositing User: Unnamed user with email knizam@utm.my
Date Deposited: 04 Jul 2013 07:10
Last Modified: 04 Jul 2013 07:10
URI: http://ir.fsksm.utm.my/id/eprint/1619

Actions (login required)

View Item View Item