Anne-Mette K. Hein
(Anne-Mette Krabbe Pedersen)
Research
I am a postdoc in the Department of Epidemiology and Public Health at Imperial College, and a member of the BGX collaboration between our department, the Statistics group in Bristol and the Imperial College Microarray Centre, which is developing flexible Bayesian models for analysing gene expression (microarray) data. My work so far has been focused on Affymetrix GeneChip arrays. We have developed Bayesian Hierarchical models for estimating gene expression levels from probe-level (using PMs and MMs) GeneChip data. A primary aim of the work is to develop a framework in which different steps in the analysis (e.g. background removal, normalisation, gene expression level estimation and differential expression) are considered simultaneously, rather than separately as is usually done.
Prior to this I worked as a post doc. with Jens Ledet Jensen at the Department of Theoretical Statistics, Institute of Mathematics, at the University of Aarhus in Denmark. We worked on developing models and methodologies for the statistical evolutionary analysis of DNA sequences with dependent rates of substitutions, within the Maximum Likelihood framework. This was a continuation of a work started during my PhD at the Department of Ecology and Genetics, Institute of Biology, at the University of Aarhus. My PhD work was within the field of molecular evolution, with focus on modelling the substitution processes in DNA sequences in general. During my PhD I spent half a year at Cambridge University working with Nick Goldman.
Microarray Work
Publications
Hein, A.-M. K., Richardson, S., Causton, H. C., Ambler, G. K. and P. J. Green (2004) A fully Bayesian Gene Expression index for Affymetrix GeneChip Data. (submitted)
Presentations
Invited talk at Complex Stochastic Systems in Biology and Medicine, Munich, October 2004. BGX: A fully Bayesian Gene eXpression index for Affymetrix GeneChip arrays
Invited talk at Rothamsted, September 2004. BGX: A fully Bayesian Gene eXpression index for Affymetrix GeneChip arrays
Invited talk at MASAMB, March 2004. A fully Bayesian method for estimating gene expression levels from Affymetrix GeneChip arrays
Presentation at the RSS Workshop, Wye July 2003. Bayesian Hierarchical models for analysing Affymetrix gene expression arrays using probe level data pdf
Molecular evolution:
Publications
Pedersen, Anne-Mette K. and Jens L. Jensen (2001). A dependent rates model and MCMC based methodology for the Maximum Likelihood analysis of sequences with overlapping reading frames. Molecular Biology and Evolution, 18(5):763-776. journal page
Forsberg, Roald, Martin B. Oleksiewicz, Anne-Mette K. Pedersen, Jotun Hein, Anette Botner and Torben Storgaard (2001). A molecular clock dates the common ancestor of European-type porcine reproductive and respiratory syndrome virus at more than ten years before the emergence of the disease. Virology, Vol. 289, issue 2: 174-179. journal page
Jensen, Jens L. and Anne-Mette K. Pedersen (2000). Probabilistic models of DNA sequence evolution with context dependent rates of substitution. Advances in Applied Probability, Vol. 32, 499-517. journal page
Yang, Z., R. Nielsen, N. Goldman and Anne-Mette K. Pedersen (2000). Codon-substitution models for variable selection pressure at amino acid sites. Genetics, 155:431-449. pdf
Pedersen, Anne-Mette K., Carsten Wiuf and Freddy B. Christiansen (1998). A codon-based model designed to describe Lentiviral Evolution. Molecular Biology and Evolution, 15(8):1069-1081. pdf
Presentations
Invited talk at Workshop in Bioinformatics and Statistical Genetics, Gothenburg, Sweden (May 2000)
Invited talk at Mathematische Stochastik Meeting, Mathematische Forschungsinstitut, Oberwolfach, Germany (March 2000)
My CV
Educational and occupational background
Teaching assistant in:
Probability theory 1 (undergraduate course in mathematics)
Biostatistics (undergraduate course in biology)
Probability theory 2 (undergraduate course in theoretical statistics)
Population biology (master level course in biology)
Evolutionary sequence analysis (master level course in biology)
Structural sequence analysis (master level course in biology)