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Development of bioinformatics methods for data integration from various diagnostic types in clinical oncology


Project Description

In the state of the art personalised therapy planning for oncology patients multiple examinations and laboratory tests are performed to get a thorough characterisation of a tumour. These examinations include all the various types of NGS based analysis such as fusion gene detection, mutation profiling, gene expression profiling, methylation profiling or micro RNA profiling, but also other pathological examination such as PET scan. Each of the analysis produces a huge and complex data that requires a specialised non-trivial computer processing to extract useful information. However, the real bioinformatics challenge is to integrate these data into a single model which would be able to make use of mutual information between different data sources and provide a comprehensive assessment of patients risks. The aim of the PhD study will be to develop such bioinformatics methods and machine learning models that will be capable of combining the various data into a single comprehensive result. The developed methods should be able to capture the ’expert knowledge’, and close collaboration with clinical genetics and other medical professionals will be necessary.
Literature:
1. Barros-Silva, D. et al. (2018) ‘Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications’, Genes, 9(9). doi: 10.3390/genes9090429.
2. Ramaswamy, V. et al. (2016) ‘Risk stratification of childhood medulloblastoma in the molecular era: the current consensus’, Acta neuropathologica. Springer, 131(6), pp. 821–831.
3. Bystry, V. et al. (2015) ‘ARResT/AssignSubsets: a novel application for robust subclassification of chronic lymphocytic leukemia based on B cell receptor IG stereotypy’, Bioinformatics , 31(23), pp.
4. 3844–3846.
5. Mertens, F. et al. (2015) ‘The emerging complexity of gene fusions in cancer’, Nature reviews. Cancer. nature.com, 15(6), pp. 371–381.
6. Santarius, T. et al. (2010) ‘A census of amplified and overexpressed human cancer genes’, Nature reviews. Cancer. nature.com, 10(1), pp.59–64.

HOW TO APPLY: Register for this call using the registration form at http://ls-phd.ceitec.cz/http-ls-phd-ceitec-cz/
and submit required documents to receive support with the preparation of the application and all formalities. Your application package will be forwarded to the supervisor for preliminary revision.
Applicants who wish to pursue a degree at the CEITEC PhD programme must hold the equivalent of a Master’s degree (MSc) in similar field (four or five year undergraduate degree). The application can be submitted before obtaining the Master’s degree, however, the applicant should obtain the degree within five month after the application deadline.

Funding Notes

Admission for studies, student registration for the full-time study programme and proper fulfilment of student duties constitute the student’s right to a regular scholarship of 22 000 CZK (850 EUR). Information is available at View Website

Living costs and other practicalities available at View Website

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