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A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction

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dc.contributor.advisor Chiu, David Paul, Jasmin 2012-03-09 2012-04-11T16:47:20Z 2012-04-11T16:47:20Z 2012-04-11
dc.description.abstract The analysis of microarray data is a challenging task because of the large dimensionality and small sample size involved. Although a few methods are available to address the problem of small sample size, they are not sufficiently successful in dealing with microarray data from extremely small (~<20) sample sizes. We propose a method to incorporate information from diverse sources to analyze the microarray data so as to improve the predictability of significant genes. A transformed data set, including statistical parameters, literature mining and gene ontology data, is evaluated. We performed classification experiments to identify potential allergy-related genes. Feature selection is used to identify the effect of features on classifier behaviour. An exploratory and domain knowledge analysis was performed on noisy real-life allergy data, and a subset of genes was selected as positive and negative class. A new set of transformed variables, depending on the mean and standard deviation statistics of the data distribution and other data sources, was identified. Significant allergy- and immune-related genes from the microarray data were selected. Experiments showed that classification predictability of significant genes can be improved. Important features from the transformed variable set were also identified. en_US
dc.language.iso en en_US
dc.subject Reclassiifcation, microarray data analysis, small sample size, allergy prediction en_US
dc.title A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction en_US
dc.type Thesis en_US Computer Science en_US Master of Science en_US Department of Computing and Information Science en_US

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