Dr Mattias Rantalainen
Biographical Sketch
I currently hold a Medical Research Council (MRC) Special Training Fellowship in Biomedical Informatics (2009-2012). Prior to my current position I had a postdoctoral position working on the data analysis work package for the MolPAGE consortium in Prof Chris Holmes' group at the Department of Statistics in Oxford. I completed my PhD at Imperial College London where I developed novel multivariate pattern recognition methods with applications in metabonomics, working together with Professor Elaine Holmes and Professor Jeremy Nicholson. I have an undergraduate degree in Engineering Biology (combined BSc/MSc) from Umea University in Sweden.
Research Interests
Robust statistics, Bayesian statistics, graphical models and predictive multivariate pattern recognition methods with applications in systems biology and genomic epidemiology.
About my research
My main research interest is in development and application of robust statistical methodologies for integrative and functional genomics modelling in genomic epidemiology. I am particularly interested in integrative modelling of clinical data and multivariate data from multiple molecular phenotype platforms.
The most commonly applied statistical methodologies, used widely for the analysis and modelling of post-genomic data, may be sensitive to even small deviation from the underlying model assumptions, such as normality (Gaussianity) of quantitative traits. However, these assumptions are often highly idealized, and thus commonly violated in practical applications. Such violations may result in spurious statistical models and results, unless careful assessments of individual models are carried out. However, manual model inspection may be both time-consuming and potentially subjective. Robust statistical models enable improved inference when data are heavy-tailed distributed and when outliers are present, which are common properties of post-genomic data.
The biological focus of my research is directed towards modelling of molecular mechanisms associated with obesity and related metabolic disorders. Obesity is a growing public health problem associated with increased risk of cardiovascular disease, type-2 diabetes and increased mortality. Adult weight and risk for obesity are highly heritable traits, but limited progress has been made towards finding specific underlying genetic variants and molecular mechanisms associated with obesity and related metabolic disease. Integrative modelling provides a route for combining information across molecular profiling platforms and clinical data, with the objective of advancing the understanding of disease-related molecular mechanisms.
Selected Publications
2008
Bylesjö M, Rantalainen M, Nicholson JK, Holmes E, Trygg J: K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space. BMC Bioinformatics 2008, 9(1):106
Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H, Zhang Y, Shen J, Pang X, Zhang M, Wei H, Chen Y, Lu H, Zuo J, Su M, Qiu Y, Jia W, Xiao C, Smith LM, Yang S, Holmes E, Tang H, Zhao G, Nicholson JK, Li L, Zhao L: Symbiotic gut microbes modulate human metabolic phenotypes. P Natl Acad Sci USA 2008, 105(6):2117-2122
2007
Rantalainen M, Bylesjö M, Cloarec O, Nicholson JK, Holmes E, Trygg J: Kernel-based orthogonal projections to latent structures (K-OPLS). J Chemometrics 2007, 21(7-9):376-385
2006
Bylesjö M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J: OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemometrics 2006, 20(8-10):341-351
Williams RE, Lenz EM, Rantalainen M, Wilson ID: The comparative metabonomics of age-related changes in the urinary composition of male Wistar-derived and Zucker (fa/fa) obese rats. Mol Biosyst 2006, 2(3-4):193-202
Newt
Pinfox