Computational Methods in Genomic and Proteomic Data Analysis

Peter Johansson

Research output: ThesisDoctoral Thesis (compilation)

8 Downloads (Pure)


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.
Original languageEnglish
Awarding Institution
  • Computational Biology and Biological Physics
  • Ringnér, Markus, Supervisor
Award date2006 Jun 2
ISBN (Print)91-628-6852-7
Publication statusPublished - 2006

Bibliographical note

Defence details

Date: 2006-06-02
Time: 10:15
Place: Lecture Hall F Sölvegatan 14 A Lund

External reviewer(s)

Name: Wessels, Lodewyk
Title: Dr.
Affiliation: Netherlands Cancer Institute


Subject classification (UKÄ)

  • Biophysics


  • Physics
  • Microarray
  • Cancer
  • Fysik
  • Classification


Dive into the research topics of 'Computational Methods in Genomic and Proteomic Data Analysis'. Together they form a unique fingerprint.

Cite this