Big data in the Life Sciences

Data: 15-19 October 2018

Information below is from the 2017 edition.

Background

Technological and scientific advances have pushed the Life sciences into the BIG data era. The increased volume and heterogeneity of available data challenges scientists to master techniques for comprehensive data analysis. Extracting meaningful (biological) information from large datasets is increasingly becoming a challenge in all fields of life sciences. Thus, the ability to select and deploy analysis tools and algorithms has become an indispensable skill for all researchers. In this course, we aim to introduce participants to techniques for comprehensive data analysis of large, heterogeneous datasets and extract relevant information for elucidating biological design principles. The course is modular and focuses on data generation, mining, analysis, data integration, and visualization.

Target group

This course is aimed at anyone working with big datasets in the life sciences who has an interest in learning more about tools and possibilities for big data analysis. The course is introductory and no specific prerequisites are asked.

Required knowledge:
Basic knowledge of statistics (mean and variance), experience of computer programming would be useful but is not mandatory.

Course aim

The aim of this course is to introduce participants to techniques for comprehensive data analysis of big data and to integrate heterogeneous data sets in order to extract relevant information for elucidating the living system. The course is modular and focuses on data generation, mining, analysis, data integration, and visualization

Lecturers

All lecturers are from the Systems & Synthetic Biology group of Wageningen University & Research:
* Dr. Edoardo Saccenti
* Dr. Rob Smith
* Dr. Maria Suarez Diez
* Jasper Koehorst MSc

Edoardo Saccenti is an expert on multivariate data analysis. His research focuses on reduction and modelling techniques for large biological data sets using random matrix theory and (sparse) component approaches.

Maria Suarez Diez has extensive experience in reverse engineering of regulatory networks, metabolic modelling and the combination thereof to gain systems level understanding of the living system. She has also extensive experience in the integration of heterogeneous data sets and in the use of semantic web technologies for data integration and sharing in the life sciences.

Rob Smith is an expert in mathematical modelling of biological networks. His research focusses on understanding how dynamical systems relate to observed phenomena.

Jasper Koehorst has ample experience on infrastructure development for big data applications for bacterial genomics. He is an expert on semantic data integration and on the use of semantic resources in the life sciences

Selected Publications:
- Camacho, J.; Saccenti, E., Group-wise PCA for exploratory data analysis Journal of computational and graphical statistics 2016, In press.
- Koehorst, J. J.; van Dam, J. C. J.; van Heck, R. G. A.; Saccenti, E.; dos Santos, V. A. P. M.; Suarez-Diez, M.; Schaap, P. J., Comparison of 432 Pseudomonas strains through integration of genomic, functional, metabolic and expression data 2016, 6, 38699.
- Dam, Jesse CJ van, Jasper J. Koehorst, Peter J. Schaap, Vitor AP Martins dos Santos, and Maria Suarez-Diez. 2015. “RDF2Graph a Tool to Recover, Understand and Validate the Ontology of an RDF Resource.” Journal of Biomedical Semantics 6: 39. doi:10.1186/s13326-015-0038-9.
- Dam, Jesse CJ van, Peter J. Schaap, Vitor AP Martins dos Santos, and Maria Suarez-Diez. 2014. “Integration of Heterogeneous Molecular Networks to Unravel Gene-Regulation in Mycobacterium Tuberculosis.” BMC Systems Biology 8 (1): 111.

Programme

The course will be held on Monday 15 till Thursday 19 October 2018.

Contact information

For information about the course contents please contact Dr. Maria Suarez Diez (maria.suarezdiez@wur.nl), Dr Edoardo Saccenti (edoardo.saccenti@wur.nl) or Dr Robert Smith (robert1.smith@wur.nl)

For organisational matters please contact Mrs. Ingeborg van Leeuwen-Bol (ingeborg.vanleeuwen-bol@wur.nl)

Registration & course fee

Registration will be made available in due time.