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Comparison of Visualization Methods of Genome-wide SNP Profiles in Childhood Acute Lymphoblastic Leukaemia
Al-Oqaily, A., Kennedy, P.J., Catchpoole, D.R. and Simoff, S.J.
Data mining and knowledge discovery have been
applied to datasets in various industries including
biomedical data. Modelling, data mining and visualization
in biomedical data address the problem of
extracting knowledge from large and complex biomedical
data. The current challenge of dealing with such
data is to develop statistical-based and data mining
methods that search and browse the underlying patterns
within the data. In this paper, we employ several
data reduction methods for visualizing genome-wide Single Nucleotide Polymorphism (SNP) datasets
based on state-of-art data reduction techniques. Visualization
approach has been selected based on the
trustworthiness of the resultant visualizations. To
deal with large amounts of genetic variation data, we
have chosen to apply different data reduction methods
to deal with the problem induced by high dimensionality.
Based on the trustworthiness metric we found
that neighbour Retrieval Visualizer (NeRV) outperformed
other methods. This method optimizes the
retrieval quality of Stochastic neighbour Embedding.
The quality measure of the visualization (i.e. NeRV)
showed excellent results, even though the dataset was
reduced from 13917 to 2 dimensions. The visualization
results will assist clinicians and biomedical researchers
in understanding the systems biology of patients
and how to compare different groups of clusters
in visualizations. |
Cite as: Al-Oqaily, A., Kennedy, P.J., Catchpoole, D.R. and Simoff, S.J. (2008). Comparison of Visualization Methods of Genome-wide SNP Profiles in Childhood Acute Lymphoblastic Leukaemia. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick, J. F., Li, J., Christen, P. and Kennedy, P. J., Eds. ACS. 111-121. |
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