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We are currently working on a project called the Human Cancer DNA Methylation Marker Atlas. The aim of this atlas is to provide the research community with a single resource to find and evaluate published DNA methylation markers for human cancer, thereby spreading knowledge and awareness of existing, experimentally validated markers and marker assays. Our motivation comes from the fact that less than 1% of published biomarkers actually end up in a commercially available test (Koch et al, 2018) and we think we can improve this number. A resource such as our atlas would promote the reuse of published markers, eliminating for example the laborious process of redesigning previously published primers. It would provide an incentive to improve the scientific quality of marker research and it would encourage replication and validation of marker studies. The ultimate goal of our project is to improve the clinical translation of DNA methylation markers, to increase the quality and number of markers available to the cancer patients.
Developing this atlas will involve the extraction of experimental and clinical data from the text of scientific publications, developing a web-based decision support tool to help users find, assess, and prioritize biomarkers for clinical translation. The group of Prof. Manon van Engeland is specialized in developing diagnostic and prognostic DNA methylation biomarkers for human cancer and therefore possesses the relevant domain-specific knowledge. The group of Prof. Michel Dumontier, has the necessary experience in developing AI technologies that help us better understand the vast amounts of biomedical data. In the proposed position you would be working on the development of the atlas, sharing your time between both groups.
A full-time postdoctoral position at Maastricht University for an initial duration of 18 months
The opportunity to develop a real-life application on the crossroads of data science and biomedical research
A diverse and international group of colleagues
A stimulating and supportive environment in which collaboration, publishing and conference attendance are highly encouraged
We are looking for someone with:
A relevant background (PhD) in data science, bio-informatics or a related field (such as computer science)
Excellent communication skills
The ability and drive to work independently
Proficient English language skills (both writing and speaking)
The proposed position requires experience with:
Text mining (natural-language processing and information extraction) in the context of (bio)curation
One or more scripting languages such as R or python, or any relevant programming experience
Database management (relational and/or graph databases)
It would be great if you were familiar with:
The basics of cancer biology and epigenetics
Large scale data integration and data mining, including machine learning/deep learning
Semantic web technologies (RDF, OWL, SPARQL, R2RML…)
The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply.
For more information look at the website www.maastrichtuniversity.nl , Support/UM employees/Employment conditions.
Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 16,300 students and 4,300 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Science and Engineering, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.
GROW focuses on research and teaching of (epi)genetic, cellular and (micro)environmental factors and mechanisms underlying normal (embryonic and fetal) and abnormal (cancer) development, with emphasis on translational research aiming at innovative approaches for individualizing prevention, patient diagnostics and treatment.