SMS Workshop 2020
“AI for Metabolomics - A hands on introduction”
October 13, 2020, 1pm-4pm
This workshops is open for students and scientists who are familiar with metabolomics methods, including raw data acquisition and processing, as well as with the use of R scripts.
The workshop starts with a reflection on AI – what is behind the abbreviation – and on the current gap preventing the application of AI metabolomics. This will be followed by the introduction of machine learning algorithms ready to use for metabolomics, including executable case studies. The participants will have the opportunity to apply R scripts on metabolomics data sets to get a scent of AI and to step into an exciting development. After the workshop, the participants will know how to prepare metabolomics data sets useful for AI and how to build first simple models for metabolite pattern recognition and prediction based on metabolomics data.
Zoom meeting on invitation
13th of October 2020, 1pm – 4pm
Pattern recognition & classification
Classifier building and testing (modelling)
Prepared slides and/or recorded slide presentations
R markdown and data from GitHub repository
PC or laptop with high band internet access
RStudio installed (R GUI also possible)
We will accept a limited number of participants (12 max) who deliver a short attendance motivation by e-mail to firstname.lastname@example.org latest by Friday the 9th of October 2020.
Dr. Endre Laczko, FGCZ at ETHZ/UZH, Metabolomics Team Leader
MORE SMS WorkshopS to be announced. Stay tuned!
FGCZ Metabolomics Course
Mass spectrometry-based metabolomics – from theory to practice
23rd - 26th March 2020
In this hands-on course, we will provide an introduction to metabolomics, explain why we want to study the metabolome and describe the current challenges in analyzing metabolites in a biological system. We will describe the multidisciplinary approach adopted in metabolomics workflows and demonstrate how the combined effort of scientists from different disciplines (analytics, biochemistry and bioinformatics) is advancing this exciting field. The course is designed to bring theory and practice together, enabling the participants to apply metabolomics in the context of their research. Didactically we will follow the "Research based teaching and learning“ concept (RBTL).
More information and registration at here.
SMS Workshop – Untargeted metabolomic data processing and analysis
“From raw data to peak tables and beyond”
November 5, 2019, Insel Spital Bern
This workshop is open for advanced students and scientists who are familiar with the basics of metabolomic technologies and the use of R scripts. The workshop starts with an introduction to metabolomic data analysis and an overview of R packages relevant for metabolomics and lipidomics. The conversion of proprietary raw data formats to open exchange raw data formats will be discussed too. The introduction lecture will be followed by hands-on sessions on the use of two R packages: MetaboAnalystR 2.0 and cosmiq. The participants will process different types of raw data into peak tables (LC-MS, DI/FI-MS and IMS-MS data raw data sets). In a final hands-on session, the participants will have the opportunity to learn and practice the proper use of MetaboAnalyst tools for normalization, transformation and analysis of peak data tables.
The detailed program is available here.
We will accept a limited number of participants who deliver a short motivation letter by e-mail to email@example.com latest by the 31st of October 2019.
The course is free for SMS members.
For non members a fee of CHF 50.- incurs:
Swiss Metabolomics Society - Adliswil
IBAN:CH95 0900 0000 8970 0322 0
Participants have to bring their own portable PCs
Make sure that you can connect to the internet by eduroam https://www.eduroam.org/about/connect-yourself
R (original R console or R Studio) should be installed before the course, as well as the two R packages MetaboAnalystR 2.0 and cosmiq; in case of difficulties, consult firstname.lastname@example.org
Participants are free to work on their own raw data or on raw data provided during the course; own raw data should be converted into an open exchange format, preferably netCDF or alternatively mzData
Active Sponsoring Members