Graduation Year

2019

Document Type

Thesis

Degree

M.A.

Degree Name

Master of Arts (M.A.)

Degree Granting Department

Mathematics and Statistics

Major Professor

Lu Lu, Ph.D.

Committee Member

Mingyang Li, Ph.D.

Committee Member

Dymtro Savchuk, Ph.D.

Keywords

Gradient Boosting, Oncology, Survival Analysis, statistics, classification, machine learning

Abstract

Cancer is one of the most deadly diseases that the world has been fighting against over decades. An enormous number of research has been conducted, via a wide scale of approaches, raging from genetic analysis to mathematical modeling. Survival analysis is a well-performed methodology frequently used to estimate the survival probability of a patient. Although there has been a large number of methods for survival analysis, efficient exploration of a high-dimensional feature space has been challenging due to its computational cost and complexity. This thesis adapts the component-wise gradient boosting algorithms for cancer survival analysis, and also proposes a new gradient boosting algorithm based on optimizing the Brier’s score. The new method is illustrated with the analysis of the microarray data of diffuse large B-cell lymphoma (DLBCL). The new gradient boosting approach not only has identified similar important biomarkers as previous statistical studies on the same data set, but also offers more insights and gained understanding on medical aspects. In addition, the performance of the new method is demonstrated through a simulation study and compared with a variety of statistical and machine learning methods.

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