We are seeking to recruit a Postdoctoral Fellow to join the Gerstung Group at the European Bioinformatics Institute (EMBL-EBI) located at the Welcome Genome Campus near Cambridge in the UK.
The group is working on high-dimensional multistage modelling of cancer progression based on genomic and clinical data.
Systematic profiling of tumours revealed that cancer is a multifactorial disease. The availability of large volumes of high-dimensional data, such as genomics, RNA expression, proteomics and also comprehensive clinical records allow us to compile an almost complete picture of relevant clinical features for each patient. The magnitude of these data, however, presents a challenge for robustly inferring the contributing factors and also accurately predicting patient outcome. We have recently developed high-dimensional models to predict disease progression through multiple states and outcomes in myeloid malignancies (1 - 3 below). Importantly, these models allow also for quantifying the effect of interventions at different stages of treatment to underpin clinical decision support.
We are looking for a postdoctoral fellow to expand our efforts both on the methodological side by developing and implementing novel statistical algorithms, but also apply these to a range of cancer types and poly-omics data sets.
1. M. Gerstung, E. Papaemmanuil, I. Martincorena, et al. (2016). Personally tailored cancer management based on knowledge banks of genomic and clinical data. bioRxiv, http://dx.doi.org/10.1101/057497
2. E. Papaemmanuil, M. Gerstung, L. Bullinger, et al. (2016). Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med, 374:2209-21. http://dx.doi.org/10.1056/NEJMoa1516192
3. M. Gerstung, A. Pellagatti, L. Malcovati, et al. (2015). Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nat Commun, 6:5901. http://dx.doi.org/10.1038/ncomms6901