Name: Tools for Climate Forecasting & AI for Cancer Immunotherapy
Start: 2023-04-20T18:30:00+02:00
End: 2023-04-20T19:30:00+02:00
Location: Barcelona Supercomputing Center - Centre Nacional de Supercomputació

Join us for an amazing workshop, co-organized with RLadies Barcelona, at the Barcelona Supercomputing Centre!

**[Register in the R-ladies event here](https://www.meetup.com/rladies-barcelona/events/292722344/)** (we have some problems with the meetup registration problems)

**Programme**
18:30 -> Introduction of R-Ladies Barcelona and BarcelonaR user group.
18:40 -> "Collaborative R tools for Climate Forecast Analysis by HPC" (An-Chi Ho and Eva Rifà)
19:00 -> "AI in R: PredIG an explainable XGBoost predictor for cancer immunotherapy" (Roc Farriol-Duran)

"**Collaborative R tools for Climate Forecast Analysis by HPC**" (An-Chi and Eva)
A large amount of data is used in the study of climate. This is why the use of HPC is necessary to carry out the analysis and
processing of climate data. In addition, multidisciplinary workflows require the use of shared tools that originate collaboration
between scientists and engineers. The talk presents some R packages developed within the Computational Earth Sciences group of
the Barcelona Supercomputing Center (BSC-CNS). It also highlights how collaboration between climate scientists and data
engineers using R tools can improve our understanding of climate patterns and lead to better predictions. The use of HPC can speed
up calculations and enable the handling of large data sets, making it a valuable tool in climate analysis and prediction.

"**AI in R: PredIG an explainable XGBoost predictor for cancer immunotherapy**" (Roc)
AI in R is (of course) possible. XGBoost is a state-of-the-art algorithm (Kaggle winner, specially working on tabular data) based on
gradient boosting and decision trees that is fully implemented in R (R-package: “xgboost”). Here, we will show an implementation on
a biomedical research question: how to predict the immune response against a tumor (and its mutations)?. This case will allow us to
focus on a binary classification problem with extreme class imbalance (due to the nature of real patient data) and how (re)tuning
XGBoost to deal with imbalance can make it work.

**Language**: English
**Requirements**: There are no specific requirements.

**Speakers**

**An-Chi Ho**
An-Chi is a research engineer in the Computational Earth Sciences Group in BSC-CNS. She is in charge of the development and
maintenance of the R packages in the department, which specialize in climate data analysis and data processing on HPCs. She has
master's degrees in the climate science field and extends her interest to the data engineering and analysis area.
**Eva Rifà**
Eva is a junior research engineer in the Computational Earth Sciences Group in BSC-CNS. Together with An-Chi we develop and
maintain the R packages in the Earth Sciences department. The R packages that we develop consist in a sort of tools for climate data
analysis using HPCs. She holds a bachelor's degree in Physics from the University of Barcelona and a Master's degree in Engineering
Physics from the Polytechnic University of Catalonia.
**Roc Farriol-Duran**
Roc Farriol-Duran is a PhD student at the Electronic and Atomic Protein Modelling group working on the computational models to
predict the human immune response. His work includes method development, such as PredIG, a T-cell epitope immunogenicity
predictor but also real-life implementations on vaccine design for viruses and cancer, such as the one that will be presented today.
He is the only R-user of the group.

Lluís

Barcelona R User Group

Technology

Programming in R

Data Science

R Project for Statistical Computing

Statistical Computing

Machine Learning

Data Visualization

Predictive Analytics

Data Mining

Applied Statistics

Data Analytics

Tools for Climate Forecasting & AI for Cancer Immunotherapy

Barcelona Supercomputing Center - Centre Nacional de Supercomputació

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Barcelona R User Group

Tools for Climate Forecasting & AI for Cancer Immunotherapy

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