Date of Award
Summer 2005
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Feng, Xin
Second Advisor
Struble, Craig
Third Advisor
Chen, Chin-Fu
Abstract
Biclustering is the process of discovering subsets of the original data where the features and objects have a high correlation. We present new method for biclustering, based on a combination of the minimum entropy clustering algorithm and the mean squared residue score. This approach uses the benefits of both the minimum entropy cluster algorithm and the mean squared residue score. The mean squared residue score is able to find correlations between the objects and features in a data set. This score is used to select subsets of features. The top ranked subsets are selected and clustered using the minimum entropy clustering algorithm. The subsequent biclusters discovered are then ranked using the mean squared residue score to determine which biclusters yield potentially good information. Further analysis was done on the highest ranked biclusters using gene ontology and biological analysis. The results showed that.the genes and conditions in the highest ranked biclusters have biological relationships with each other. These results were promising. With further analysis, more relationships maybe discovered.
Recommended Citation
Szeto, Jack WeiYi, "A Minimum Entropy Approach to Biclustering and Application to Mine Microarray Gene Expression Data" (2005). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4207.
https://epublications.marquette.edu/theses/4207