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Computational Methods in Genomic and Proteomic Data Analysis

Johansson, Peter LU (2006)
Abstract (Swedish)
Popular Abstract in Swedish

Med modern mätteknik kan vi mäta cellers egenskaper för alla gener samtidigt. För att tolka den stora datamängden krävs analysmetoder och datorverktyg. Den här avhandlingen behandlar ett antal sådana verktyg avsedda att klargöra geners och proteiners inbördes samband. En metod att hantera datavärden av varierande kvalité presenteras, såväl som ett verktyg att visualisera samband i masspektrometri-data. Klassificering och då speciellt ensemblemetoder diskuteras och används för att undersöka två signalvägar, MAPK och PI3K, som är viktiga i cancer.
Abstract
With the great progress of technology in genomics and proteomics generating an exponentially increasing amount of data, computational and statistical methods have become indispensable) for accurate biological conclusions. In this doctoral dissertation, we present several algorithms designed to delve large amounts of data and bolster the understanding of molecular biology. MAPK and PI3K, two signaling pathways important in cancer, are explored using gene expression profiling and machine learning. Machine learning and particularly ensembles of classifiers are studied in context of genomic and proteomic data. An approach to screen and find relations in protein mass spectrometry data is described. Also, an algorithm to handle unreliable values... (More)
With the great progress of technology in genomics and proteomics generating an exponentially increasing amount of data, computational and statistical methods have become indispensable) for accurate biological conclusions. In this doctoral dissertation, we present several algorithms designed to delve large amounts of data and bolster the understanding of molecular biology. MAPK and PI3K, two signaling pathways important in cancer, are explored using gene expression profiling and machine learning. Machine learning and particularly ensembles of classifiers are studied in context of genomic and proteomic data. An approach to screen and find relations in protein mass spectrometry data is described. Also, an algorithm to handle unreliable values in data with much redundancy is presented. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Dr. Wessels, Lodewyk, Netherlands Cancer Institute
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Physics, Microarray, Cancer, Fysik, Classification
pages
98 pages
publisher
Department of Theoretical Physics, Lund University
defense location
Lecture Hall F Sölvegatan 14 A Lund
defense date
2006-06-02 10:15
ISBN
91-628-6852-7
language
English
LU publication?
yes
id
80c11b17-1be3-44be-b628-939f6ec8980f (old id 546840)
date added to LUP
2007-09-05 09:37:31
date last changed
2016-09-19 08:45:07
@phdthesis{80c11b17-1be3-44be-b628-939f6ec8980f,
  abstract     = {With the great progress of technology in genomics and proteomics generating an exponentially increasing amount of data, computational and statistical methods have become indispensable) for accurate biological conclusions. In this doctoral dissertation, we present several algorithms designed to delve large amounts of data and bolster the understanding of molecular biology. MAPK and PI3K, two signaling pathways important in cancer, are explored using gene expression profiling and machine learning. Machine learning and particularly ensembles of classifiers are studied in context of genomic and proteomic data. An approach to screen and find relations in protein mass spectrometry data is described. Also, an algorithm to handle unreliable values in data with much redundancy is presented.},
  author       = {Johansson, Peter},
  isbn         = {91-628-6852-7},
  keyword      = {Physics,Microarray,Cancer,Fysik,Classification},
  language     = {eng},
  pages        = {98},
  publisher    = {Department of Theoretical Physics, Lund University},
  school       = {Lund University},
  title        = {Computational Methods in Genomic and Proteomic Data Analysis},
  year         = {2006},
}