Second International RTG Symposium Genes, Environment and Inflammation - Declaration Of Consent

Declaration of Consent to Data Processing
1. You would like to register online for the RTG 1743 Symposium 2018. By registering on the RTG website/News/2nd Symposium ( https://www.genes-environment-inflammation.de/news/second-interna-tional... ) you are indicating your interest in attend-ing the RTG 1743 Symposium 2018.

Research Training Group

genes, environment, inflammation

Within this Research Training Group we will study the environmental influences responsible for the development of complex, chronic diseases. Moreover, we will systematically examine the previously understudied interplay between the (micro)-environment and predisposing genetic factors...

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Research

The scientists in the Research Training Group (RTG) want to find out

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  • how the non-genetical schemes in the cell change during the development of chronic inflammatory diseases;
  • how diet influences the development of inflammatory diseases;
  • new statistical and bioinformatical ways to express research results.

People

a team of young and innovative scientists who are experts in the fields of:

  • molecular biology
  • human genetics
  • immunology
  • bioinformatics
  • biochemistry
  • microbiology
  • epidemiology
  • statistics.

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Education

The purpose of the structured doctoral student programme is to:

  • enable young scientists to obtain their doctoral degree within three years;
  • qualify doctoral students for postdoctoral work;
  • impart interdisciplinary knowledge;
  • promote postgraduate students' transferrable skills.

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Latest News

16 Sep, 2020
Online Training: Parallel programming in Julia

Bioinformatics is evolving rapidly into a data-oriented field, especially with the rise of omics and multi-omics studies where a single experiment can produce a few gigabytes of data. A second field that also relies heavily on data is Artificial-intelligence, specifically, machine-learning and deep learning where huge datasets are usually needed to properly train these models. Over the last couple of years, others in the community and we, have been developing and applying AI and DL tools to analyze big biological datasets, with an extremally promising results.

Nevertheless, at a deeper level, the working horses of the analysis & modeling of big data is high performance computing, HPC. Currently, two languages are dominating the field of data analysis and modeling, namely Python and R, with a rich ecosystem of analysis libraries. However, a major drawback of these languages has been the performance. As both of these languages are interpreted languages they are not usually regarded as fast languages

Different solutions have been proposed to mitigate this problem. One is the development of a new programming language, and hence Julia was born. Julia is a modern programming language first released in 2012 that combines the readability and rapid prototyping of Python with the speed of C. It utilizes Just-in-time compilation using LLVM compiler, and offers support for different parallel-computing techniques, like multithreading, multiprocessing, and...

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