Automated Workflow Composition in the Life Sciences
9 - 13 March 2020
Leiden, NetherlandsAutomated Workflow Composition in the Life Sciences https://www.lorentzcenter.nl/lc/web/2020/1201/info.php3?wsid=1201&venue=Oort https://tess.elixir-europe.org/events/automated-workflow-composition-in-the-life-sciences In the age of computational science, researchers in the life sciences – just as in other domains – regularly face the need of composing several individual software tools into pipelines or workflows that perform the specific data analysis processes that they need in their research. For over 20 years now, dedicated scientific workflow management systems have been supporting scientists in this task, and they continue to gain popularity. In fact, recent years have seen significant progress in the functional annotation of bioinformatics software tools, as well as their virtualization, containerization and assembly into workflows for automatically executing the processes. At least since the rise of the Semantic Web in the early 2000s, also the idea of semantics-based automated composition of workflows has been around to simplify the work with scientific workflows further and free life science researchers from having to deal with the technicalities of software composition. This would not only save valuable research time, but also reduce errors, allow benchmarking of data analysis pipelines and enable new scientific findings by discovering workflows that researchers would not have thought of themselves. However, despite its obvious potential and appeal, the need for optimizing data analysis workflows, and despite different research groups working on the topic, automated workflow composition has not yet arrived in the daily practice of life science researchers. The reasons for this are manifold. Some are more practical (for example the lack of automatic composition tools in the commonly used software frameworks), others are of more fundamental nature (such as questions on specification languages, composition algorithms, formal semantics and workflows representations). On one important aspect, namely the semantic annotation of tools on a large scale, the life science community has made significant progress in the last years: The EDAM ontology provides a controlled vocabulary of bioinformatics operations, data types and formats, and the bio.tools registry has become a large collection of bioinformatics tools that are semantically annotated with terms from the EDAM ontology. As demonstrated in a recent Bioinformatics publication (https://academic.oup.com/bioinformatics/article/35/4/656/5060940), this forms a solid basis for performing automated workflow composition in the life sciences domain. Nevertheless, it is still a long way to its use in daily scientific practice. This workshop will bring together researchers and practitioners who have been working on different aspects related to automated workflow composition in the life sciences. These include life science researchers, tool providers, infrastructure developers, ontologists, algorithmics researchers and many more. They do not normally come together as a group at the regular scientific events, so a Lorentz workshop devoted to this topic provides a unique opportunity to join forces and together significantly advance the field. 2020-03-09 09:00:00 UTC 2020-03-13 17:00:00 UTC Jon Ison, Anna-Lena Lamprecht, Magnus Palmblad and Veit Schwämmle Lorentz Center Oort, Leiden, Netherlands Lorentz Center Oort Leiden Netherlands 2333 Omics Workflows Leiden University email@example.com ELIXIRLorentz CenterLUMC software developers, bioinformaticiansbiocurators 50 workshops_and_courses  
15 - 17 November 2021
Oeiras, PortugalMachine Learning http://biodata.pt https://tess.elixir-europe.org/events/machine-learning With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially in recent times and a major issue is to mine useful knowledge from these data which are also heterogeneous in nature. Machine learning (ML) is a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine learning can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics. This 3 day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets. Instructors: Vandrille Duchemin, University of Basel, CH Crhistian Cardona, University of Tuebingen, DE 2021-11-15 09:30:00 UTC 2021-11-17 18:00:00 UTC Pedro Fernandes Instituto Gulbenkian de Ciência, Oeiras, Portugal Instituto Gulbenkian de Ciência Oeiras Portugal 2780-156 Machine learning Instituto Gulbenkian de Ciência firstname.lastname@example.org To register your interest, please send an e-mail to email@example.com with "Machine Learning" in the subject line UNTIL November 11th 2021, stating the reason why you would be interested in attending this course in a single paragraph. ELIXIR PhD Studentspost-docsScientistsstudents 17 workshops_and_courses registration_of_interest Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression
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