Data Mining the Genetics of Leukemia
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Authors
Morton, Geoffrey
Date
2010-01-13T15:28:42Z
Type
thesis
Language
eng
Keyword
Data Mining , Acute Lymphoblastic Leukemia
Alternative Title
Abstract
Acute Lymphoblastic Leukemia (ALL) is the most common cancer in children under
the age of 15. At present, diagnosis, prognosis and treatment decisions are made
based upon blood and bone marrow laboratory testing. With advances in microarray
technology it is becoming more feasible to perform genetic assessment of individual
patients as well. We used Singular Value Decomposition (SVD) on Illumina SNP,
Affymetrix and cDNA gene-expression data and performed aggressive attribute se-
lection using random forests to reduce the number of attributes to a manageable
size. We then explored clustering and prediction of patient-specific properties such
as disease sub-classification, and especially clinical outcome. We determined that
integrating multiple types of data can provide more meaningful information than
individual datasets, if combined properly. This method is able to capture the cor-
relation between the attributes. The most striking result is an apparent connection
between genetic background and patient mortality under existing treatment regimes.
We find that we can cluster well using the mortality label of the patients. Also, using
a Support Vector Machine (SVM) we can predict clinical outcome with high accu-racy. This thesis will discuss the data-mining methods used and their application to
biomedical research, as well as our results and how this will affect the diagnosis and
treatment of ALL in the future.
Description
Thesis (Master, Computing) -- Queen's University, 2010-01-12 18:40:44.2
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