RUcore Resource Object
RUcore Resource Object
PDF-1(2.85 MB)
TitleCombinatorial pattern-based survival analysis with applications in biology and medicine
NameReddy, Anupama Rajasekhara (author), Boros, Endre (chair), Hammer, Peter (co-chair), Bhanot, Gyan (internal member), Jeong, Myong (internal member), Stratila, Dan (internal member), Alexe, Gabriela (outside member), Rutgers University, Graduate School - New Brunswick,
Degree Date2009-10
Date Created2009
SubjectOperations Research,
Survival analysis (Biometry),
Medical statistics
DescriptionIn the current era of targeted therapies and personalized medicine, survival analysis (predicting survival time of patients) is a very important problem. Survival analysis is similar to regression except for the presence of censored observations (observations with incomplete survival time information). We propose to use a combinatorial pattern-based methodology, Logical Analysis of Data (LAD), for survival analysis. LAD is a two-class classification method. In this thesis we extend LAD for survival analysis in various ways. Our first approach is to define high- and low-risk patients, and reduce the problem to two-class classification. This approach is particularly useful for datasets with a large number of samples, and small number of features. In datasets where the feature space is high-dimensional (for example, gene expression data), we first used an unsupervised clustering approach to identify robust clusters in the data, the hypothesis being that the different clusters are associated with different survival profiles. We present a linear programming model to predict survival. Finally, we develop a new method, Logical Analysis of Survival Data (LASD), and validate it on a kidney cancer dataset. Ensemble methods are presented to improve the robustness of LASD.
NotePh.D.
NoteIncludes bibliographical references (p. 134-143)
Noteby Anupama Rajasekhara Reddy
Genretheses
Persistent URLhttp://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work