Date of Award
Thesis - Restricted
Master of Science (MS)
Electrical and Computer Engineering
Povinelli, Richard J.
Struble, Craig A.
A novel method for temporal profiling of short time series microarray data, inspired by reconstructed phase space (RPS) theory and the minimum entropy clustering algorithm is introduced. The augmented first difference space provides addition information, relating to the trend of the time series, for the clustering algorithm. This resultant higher dimension space is clustered using the minimum entropy clustering algorithm. The means of the subsequent clusters are then translated into a trend matrix, and a k-means clustering algorithm is applied to group the trends together. The results showed the proposed method was able to group genes with similar trends together and comparing with current approaches this new approach is able to find tighter clusters within the data.
Szeto, Peter Wei De, "A Temporal Difference Representation for Temporal Profiling of Short Time Series Microarray Gene Expression Data" (2007). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4197.