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Combinatorial pattern-based survival analysis with applications in biology and medicine
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Combinatorial pattern-based survival analysis with applications in biology and medicine
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ETD_1993
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051891
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eng
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theses
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Operations Research
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Topic
Survival analysis (Biometry)
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Topic
Medical statistics
Abstract
In 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.
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electronic resource
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xii, 144 p. : ill.
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Ph.D.
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Includes bibliographical references (p. 134-143)
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by Anupama Rajasekhara Reddy
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Reddy
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Anupama Rajasekhara
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1981-
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Anupama Rajasekhara Reddy
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Endre Boros
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Hammer
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Peter
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Peter L Hammer
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Bhanot
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Gyan
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Gyan Bhanot
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Jeong
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Myong
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Myong K Jeong
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Stratila
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Dan
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Dan Stratila
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Alexe
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Gabriela
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Gabriela Alexe
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3PG1RXQ
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ETD doctoral
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The author owns the copyright to this work
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Open
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Anupama
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Anupama Reddy
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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