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Computational approaches to identifying molecular associations in high-throughput biological data
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Computational approaches to identifying molecular associations in high-throughput biological data
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Huang
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Yang
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1976-
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Yang Huang
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author
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Farach-Colton
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Martin
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Advisory Committee
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Martin Farach-Colton
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chair
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Kulikowski
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Casimir
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Advisory Committee
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Casimir Kulikowski
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internal member
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Pavlovic
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Vladimir
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Advisory Committee
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Vladimir Pavlovic
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internal member
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Singh
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Mona
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Advisory Committee
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Mona Singh
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
Language
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English
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electronic
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xii, 89 pages
Abstract
Biomolecules, such as proteins and nucleic acids, are the building blocks of living organisms.
Their complex interactions and associations are the key to understanding the basic mechanisms of life. Recently, high-throughput biological experiments allow to study thousands of biomolecules simultaneously, yielding a large amount of data that may reveal essential molecular associations. The work in this dissertation will focus on analyzing protein-protein interaction and gene-expression data obtained from these experiments.
To identify temporal associations among proteins in pathways, the temporal order, by which proteins enter and exit the pathways, is needed. For this purpose, an interval graph model is presented for molecular pathways using protein-protein interactions.
Based on this model, a tool, XRONOS, is developed to compute possible orderings of
proteins in pathways. XRONOS is then applied to the yeast ribosome assembly pathway and develop several tests based on graph theory, statistics and biological knowledge to validate the computed orderings.
In a gene-expression matrix, rows correspond to genes and columns correspond to measuring conditions. An association coefficient is defined for a pair of genes in a discretized gene-expression matrix. These association coefficients are then applied to define dissimilarity measure between two discretized gene-expression matrices. We are able to effectively compute the dissimilarity between gene-expression matrices using concept lattices. With the dissimilarity measure, a tool, LABSTER, is developed to cluster a set of gene-expression matrices for class discovery. LABSTER is successfully used on simulation and clinical gene-expression data sets to discover different cell phenotypes.
Since concept lattices prove useful in many areas and the size of them can be exponential
to the input, it has become important to construct concept lattices efficiently.
An algorithm is designed with delay-time complexity O(|G||M|) given an input binary matrix of size |G||M|. Based on the characterization of irregular concepts, the algorithm improves the previous best delay-time complexity O(|G||M|2). In addition, a method to represent concept lattices in a compact representation is proposed. This method can save storage space compared to the full representation normally used. The algorithm for the full representation of concept lattices is modified to generate a compact
representation.
Their complex interactions and associations are the key to understanding the basic mechanisms of life. Recently, high-throughput biological experiments allow to study thousands of biomolecules simultaneously, yielding a large amount of data that may reveal essential molecular associations. The work in this dissertation will focus on analyzing protein-protein interaction and gene-expression data obtained from these experiments.
To identify temporal associations among proteins in pathways, the temporal order, by which proteins enter and exit the pathways, is needed. For this purpose, an interval graph model is presented for molecular pathways using protein-protein interactions.
Based on this model, a tool, XRONOS, is developed to compute possible orderings of
proteins in pathways. XRONOS is then applied to the yeast ribosome assembly pathway and develop several tests based on graph theory, statistics and biological knowledge to validate the computed orderings.
In a gene-expression matrix, rows correspond to genes and columns correspond to measuring conditions. An association coefficient is defined for a pair of genes in a discretized gene-expression matrix. These association coefficients are then applied to define dissimilarity measure between two discretized gene-expression matrices. We are able to effectively compute the dissimilarity between gene-expression matrices using concept lattices. With the dissimilarity measure, a tool, LABSTER, is developed to cluster a set of gene-expression matrices for class discovery. LABSTER is successfully used on simulation and clinical gene-expression data sets to discover different cell phenotypes.
Since concept lattices prove useful in many areas and the size of them can be exponential
to the input, it has become important to construct concept lattices efficiently.
An algorithm is designed with delay-time complexity O(|G||M|) given an input binary matrix of size |G||M|. Based on the characterization of irregular concepts, the algorithm improves the previous best delay-time complexity O(|G||M|2). In addition, a method to represent concept lattices in a compact representation is proposed. This method can save storage space compared to the full representation normally used. The algorithm for the full representation of concept lattices is modified to generate a compact
representation.
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 79-87).
Subject
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Topic
Computer Science
Subject
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Topic
Bioinformatics
Subject
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Topic
Computational biology
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.16094
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doi:10.7282/T3CZ37JC
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ETD doctoral
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Open
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Yang Huang
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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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