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About me

I lead the Predictive Medicine research group at Department of Medical Epidemiology and Biostatistics.

Prior to my current position I was a research fellow (2009-2013) with a joint affiliation at the Department of Statistics (University of Oxford) and at the Wellcome Trust Centre for Human Genetics (University of Oxford) where I worked with Professor Chris Holmes and Dr. Cecilia Lindgren. I was awarded a MRC Centenary Early Career Award (2012-2013) and a Medical Research Council (MRC) Special Training Fellowship in Biomedical Informatics (2009-2012). 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 (2008).  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 Umeå University in Sweden. I was born in Västerås, Sweden, in 1978.

 

Research description

We develop and apply statistical and machine learning methodologies for predictive modelling in biomedical applications with a particular interest in precision medicine and cancer research.

 

Our mission is to enable quantitative approaches to precision medicine and to develop novel patient stratification models for prognostic and treatment predictive applications. To achieve this we develop methods and models that allow us to transform large biomedical data into clinically relevant predictions at the individual level.

 

Our research is based on large and high-dimensional datasets (big-data) across multiple modalities including comprehensive molecular profiling (e.g. DNA- and RNA-sequencing), clinical information and medical imaging data.

 

Research areas

  • Predictive models in cancer based on comprehensive molecular phenotyping
  • Single-cell sequencing in cancer
  • Application of machine learning and deep learning for medical image analysis
  • Methods for robust and integrative prediction modelling across multiple data modalities

 

Cancer

  • Molecular-based diagnostics and patient stratification in breast cancer based on DNA- and RNA-sequencing
  • Improving patient risk stratification in AML based on DNA- and RNA-sequencing
  • Data-driven prostate cancer diagnostics based on histopathology imaging

 

Single-cell molecular profiling

  • Applications of single-cell RNA sequencing for characterisation intra-tumor heterogeneity
  • Methods for analysis of  single-cell RNAseq data

Recent publications

 

Holm J., Eriksson L., Ploner A., Rantalainen M., Li J., Hall P., Czene K.. Assessment of breast cancer risk factors reveals subtype heterogeneity. Cancer Research. 2017 Jan 1:canres-2574.

Spjuth, O., Karlsson, A., Clements, M., Humphreys, K., Ivansson, E., Dowling, J., Eklund, M., Jauhiainen, A., Czene, K., Grönberg, H., Sparén, P., Wiklund, F., Cheddad, A., Pálsdóttir, þ., Rantalainen, M., Abrahamsson, L., Laure, E., Litton, J.E., Palmgren, ,J. E-Science technologies in a workflow for personalized medicine using cancer screening as a case study. J Am Med Inform Assoc 2017 ocx038.

 

Wang, M., Lindberg, J., Klevebring, D., Nilsson, C., Mer, A.S., Rantalainen, M.§, Lehmann, S.§, Grönberg, H§. Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling. Leukemia, 2017, Feb 7.

 

Rantalainen, M., Klevebring, K., Lindberg, J., Ivansson,E., Rosin, G., Kis, L., Celebioglu, F., Fredriksson, I., Czene, K., Frisell, J., Hartman, J., Bergh, J., Grönberg, H.; Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers. Scientific Reports, 2016, 6, 38037.

 

Li, J., Ivansson, E., Klevebring, D., Tobin, N.P., Lindström, L.S., Holm, J., Prochazka, G., Cristando, C., Palmgren, J., Törnberg, S., Humphreys, K., Hartman, J., Frisell, J., Rantalainen, M., Lindberg, J., Hall, P., Bergh, J., Grönberg, H., Czene, K.; Molecular differences between screen-detected and interval breast cancers are largely explained by PAM50 subtypes. American Association for Cancer Research, 2016, pp.clincanres-0967.

 

Georgoudaki, A.M., Prokopec, K.E., Boura, V.F., Hellqvist, E., Sohn, S., Östling, J., Dahan, R., Harris, R.A., Rantalainen, M., Klevebring, D. and Sund, M., Brage S.E., Fuxe J., Rolny C., Li F., Ravetch J.V., Karlsson M.C.; Reprogramming Tumor-Associated Macrophages by Antibody Targeting Inhibits Cancer Progression and Metastasis. Cell reports, 2016, 15(9), 2000-2011.

 

Mer, A.S., Klevebring, D., Grönberg, H., Rantalainen, M.; Study design requirements for RNA sequencing-based breast cancer diagnostics. Scientific reports, 2016, 6.

 

Wang, M., Klevebring, D., Lindberg, K., Czene, K., Grönberg, H., Rantalainen M.; Determining breast cancer histological grade from RNA sequencing data. Breast Cancer Research, 2016, 18(1).

 

Vu, N.T., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., Rantalainen M.§, Pawitan Y.§. Beta-Poisson model for single-cell RNA-seq data analyses. Bioinformatics, 2016, btw202.

 

Stålhammar, G., Martinez, N.F., Lippert M., Tobin, N.P., Mølholm, I., Kis, L., Rosin, G., Rantalainen, M., Pedersen, L, Bergh, J, Grunkin, M.; Digital image analysis outperforms manual biomarker assessment in breast cancer. Modern Pathology, 2016, Feb 26.

 

Teaching portfolio

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