Acute Lymphoblastic Leukemia (ALL) is the most common cancer in children under the age of 15. At present, diagnosis, prognosis and treatment decisions are made based upon blood and bone marrow laboratory testing. With advances in microarray technology it is becoming more feasible to perform genetic assessment of individual patients as well. We used Singular Value Decomposition (SVD) on Illumina SNP, Affymetrix and cDNA gene-expression data and performed aggressive attribute se- lection using random forests to reduce the number of attributes to a manageable size. We then explored clustering and prediction of patient-specific properties such as disease sub-classification, and especially clinical outcome. We determined that integrating multiple types of data can provide more meaningful information than individual datasets, if combined properly. This method is able to capture the cor- relation between the attributes. The most striking result is an apparent connection between genetic background and patient mortality under existing treatment regimes. We find that we can cluster well using the mortality label of the patients. Also, using a Support Vector Machine (SVM) we can predict clinical outcome with high accu-racy. This thesis will discuss the data-mining methods used and their application to biomedical research, as well as our results and how this will affect the diagnosis and treatment of ALL in the future. / Thesis (Master, Computing) -- Queen's University, 2010-01-12 18:40:44.2

Existing frequent-sequence mining algorithms perform multiple scans of a database, or a structure that captures the database. In this M.Sc. thesis, I propose a frequent-sequence mining algorithm that mines each database row as it reads it, so that it can potentially complete mining in the time it takes to read the database once. I achieve this by having my algorithm enumerate all sub-sequences from each row as it reads it. Since sub-sequence enumeration is a time-consuming process, I create a method to distribute the work over multiple computers, processors, and thread units, while balancing the load between all resources, and limiting the amount of communication so that my algorithm scales well in regards to the number of computers used. Experimental results show that my algorithm is effective, and can potentially complete the mining process in near the time it takes to perform one scan of the input database.

Existing frequent-sequence mining algorithms perform multiple scans of a database, or a structure that captures the database. In this M.Sc. thesis, I propose a frequent-sequence mining algorithm that mines each database row as it reads it, so that it can potentially complete mining in the time it takes to read the database once. I achieve this by having my algorithm enumerate all sub-sequences from each row as it reads it. Since sub-sequence enumeration is a time-consuming process, I create a method to distribute the work over multiple computers, processors, and thread units, while balancing the load between all resources, and limiting the amount of communication so that my algorithm scales well in regards to the number of computers used. Experimental results show that my algorithm is effective, and can potentially complete the mining process in near the time it takes to perform one scan of the input database.

Thesis (Ph.D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 98-102).

Zugl.: Wien, Wirtschaftsuniv., Diplomarb.

Thesis (M.S.) -- Central Connecticut State University, 2008. / Thesis advisor: Roger Bilisoly. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 122-127). Also available via the World Wide Web.

Zugl.: Passau, Univ., Diss., 2007

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 84-88) Also available in print.

Thesis (doctorate)--Universität Ulm, 2005. / Includes bibliographical references (p. 227-248).

Thesis (Master)--İzmir Institute of Technology, İzmir, 2002. / Includes bibliographical references (59-61).