Novel approaches to biclustering and gene functional classification in microarray gene expression data
Citation:
Kenneth Bryan, 'Novel approaches to biclustering and gene functional classification in microarray gene expression data', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2007, pp 143Download Item:
Abstract:
Microarray analysis is a high-throughput experimental technique with the capacity to
measure the expressions of thousands of genes in parallel over many experimental samples
(tissues types, environmental conditions, time points etc.). To fully exploit the large
volumes of expression data produced by these experiments requires the application of
statistical analysis and machine learning methods. Microarray datasets may contain many
genes and samples with unknown labels. New gene functional classes may also emerge as
our understanding of the underlying biological system increases. As a result, unsupervised
methods of analysis, such ais cluster analysis, often prove most useful in this domain.
Author: Bryan, Kenneth
Advisor:
Cunningham, PádraigPublisher:
Trinity College (Dublin, Ireland). School of Computer Science & StatisticsNote:
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