Statistical and Functional Analysis of Genomic and Proteomic Data
(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:
https://lup.lub.lu.se/record/25258
- author
- Liu, Yingchun LU
- supervisor
- opponent
-
- Assistant Professor Mukherjee, Sayan, Duke University, USA
- organization
- publishing date
- 2007
- 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 10:15:00
- ISBN
- 91-628-6997-3
- language
- English
- LU publication?
- yes
- id
- 954eb255-c060-4873-a47d-ccab2df0de5d (old id 25258)
- date added to LUP
- 2016-04-04 10:21:04
- date last changed
- 2023-04-18 18:41:46
@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}}, 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}}, language = {{eng}}, publisher = {{Department of Theoretical Physics, Lund University}}, school = {{Lund University}}, title = {{Statistical and Functional Analysis of Genomic and Proteomic Data}}, url = {{https://lup.lub.lu.se/search/files/117828107/Yingchun_Liu_kappa.pdf}}, year = {{2007}}, }