Workshop Schedule

Semantic Web solutions for large-scale biomedical data analytics (SeWeBMeDA-2020)

7th November 2020 (3 PM CET)

Free But Mandatory Registration (4th Nov): https://forms.gle/3K5kcmSTykzE11DCA

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3:00 - 3:15 pm (CET) Opening

3:15- 4:00 pm (CET) - Keynote by Prof. Andre Dekker,

Title: From Big Data to Better Cancer Care

Summary:

Big data, artificial intelligence, machine learning and data science are expected to rapidly change the way we practice medicine and cancer cancer specifically.

Since 2005 Maastricht University and MAASTRO Clinic have developed a research program on this topic. A global IT infrastructure has been developed in which cancer centers are being connected with currently up to 25 partners. The aim is to enable cross-institute, privacy-preserving, data sharing & machine learning and more efficient clinical evidence generation: a concept now commonly referred to as "Rapid Learning".

In the seminar innovative technology to extract, store and process (big) data for Rapid Learning and will be discussed. All this data is often seen as tremendously promising and is predicted to change health care radically, but at this point in time is mostly a challenge as we keep accumulating data without a clear path to clinical applications while privacy concerns are on the rise. Methods and examples how we go from data to making a difference in lives of cancer patients will be presented. As will the methods to do this in a way that preserves the privacy of patients.

Key words: Big FAIR Data, Federated Learning, Semantic Web, Image Mining, Cancer, Ontologies,

Short Bio

Prof. Andre Dekker, PhD (1974) is a medical physicist and professor of Clinical Data Science at MAASTRO Clinic and Maastricht University Medical Center in The Netherlands. His Clinical Data Science research group (30 staff) focuses on three main themes:

1) building global FAIR (Semantic Web based) data sharing infrastructures;

2) machine learning health outcome prediction models from this data;

3) applying prediction models to improve lives of patients and citizens

Prof. Dekker has authored over 150 publications in peer reviewed journals covering informatics, imaging, radiotherapy, tissue optics and heart disease and holds multiple awarded patents. He has held visiting scientist appointments at organizations in the UK, Australia, Italy, USA and Canada.

Session 1 Paper Presentations (4:00- 5:00) pm CET

Presentation 1: Identifying Ontology Concepts of Study Cohort Terms via NCBO Annotator

Jay Franklin, Shruthi Chari, Morgan Foreman, Oshani Seneviratne, Amar Das and Deborah McGuinness

Presentation 2: Access to care: Geographical analysis of medical facilities

Selene Baez Santamaria, Manolis Manousogiannis, Guusje Boomgaard, Linh Tran, Zoltán Szlávik and Robert-Jan Sips

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Session 2 Break Out parallel Sessions (5:00- 5:45) pm CET

Life Sciences and Biomedical Ontologies/ Taxonomies and Vocabularies.

Dietrich Rebholz-Schuhmann

Professor Dietrich Rebholz-Schuhmann is the Scientific Director of ZB MED – Information Centre for Life Sciences. At the same time, he is Professor of Information Processing, Exploitation and Support at the Medical Faculty of Cologne University. Rebholz-Schuhmann is a medical doctor and a computer scientist. His research is positioned in semantic technologies in the biomedical domain. In his previous research he has established large-scale on-the-fly biomedical text mining solutions and has contributed to the semantic normalisation in the biomedical domain. He has published more than 170 publications including contributions to high-profile journals, has been contributing to community efforts (e.g., ECCB steering committee), and is editor-in-chief of the Journal of Biomedical Semantics.

(Semantic Web/ Big Data) applications for medical, biomedical & life sciences

Prof Michel Dumontier

Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University. His research focuses on the development of computational methods for scalable integration and reproducible analysis of FAIR (Findable, Accessible, Interoperable and Reusable) data. His group combines semantic web technologies with effective indexing, machine learning and network analysis for drug discovery and personalized medicine. Previously at Stanford University, Dr. Dumontier now leads a new inter-faculty Institute for Data Science at Maastricht University with a focus on accelerating scientific discovery, improving health and well-being, and strengthening communities. He is a Principal Investigator for the NCATS Biomedical Data Translator and a co-Investigator for the NIH Data Commons. He is a co-founder of the FAIR (Findable, Accessible, Interoperable, Re-usable) data initiative, and is the scientific director for Bio2RDF, an open source project to generate Linked Data for the Life Sciences.

FAIR publishing, usage and analysis of biomedical/ life science data

Marco Roos

Marco Roos is Group leader of the LUMC BioSemantics group, Associate Professor and senior scientist. He leads the research and development of knowledge discovery methods and their application for human genetics research and biomedical applications. His focus is on improving the practices of biomedical research by emerging approaches from the field of computer science, for instance to help understand the role of the genome and chromatin in disease such as Huntington’s Disease. With experience in workflow technology and Linked Data he advises on data integration approaches at the LUMC, and in the domain of Rare Disease research.

Closing 5:45 pm CET

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