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Statistical and Functional Analysis of Genomic and Proteomic Data

Liu, Yingchun LU (2007)
Abstract
High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based... (More)
High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Assistant Professor Mukherjee, Sayan, Duke University, USA
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Bioinformatik, biomathematics biometrics, unsupervised classification, Bioinformatics, medical informatics, TGF-beta, linear mixed model, expression data, dye bias, 2D-gel, signaling pathway, regulatory motif, microarray, medicinsk informatik, biomatematik
pages
22 pages
publisher
Department of Theoretical Physics, Lund University
defense location
Lecture hall F of the Department of Physics
defense date
2007-01-26 10:15
ISBN
91-628-6997-3
language
English
LU publication?
yes
id
954eb255-c060-4873-a47d-ccab2df0de5d (old id 25258)
date added to LUP
2007-06-05 10:54:30
date last changed
2016-09-19 08:45:04
@phdthesis{954eb255-c060-4873-a47d-ccab2df0de5d,
  abstract     = {High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways.},
  author       = {Liu, Yingchun},
  isbn         = {91-628-6997-3},
  keyword      = {Bioinformatik,biomathematics biometrics,unsupervised classification,Bioinformatics,medical informatics,TGF-beta,linear mixed model,expression data,dye bias,2D-gel,signaling pathway,regulatory motif,microarray,medicinsk informatik,biomatematik},
  language     = {eng},
  pages        = {22},
  publisher    = {Department of Theoretical Physics, Lund University},
  school       = {Lund University},
  title        = {Statistical and Functional Analysis of Genomic and Proteomic Data},
  year         = {2007},
}