Applied Pharmaceutical Bioinformatics

The course is a continuation of the course “Pharmaceutical Bioinformatics” with the purpose to learn how to practically apply the predictive modeling methods introduced there.

This continuation-course provides a deepened understanding of statistical modeling methods for applications in the pharmaceutical field, life sciences and pharmacology, focusing on how to practically solve problems using different informatics methods.

The course covers:

  • Introduction to statistical modeling in pharmaceutical bioinformatics, with an in depth review of QSAR and proteochemometrics.
  • Calculation of different kinds of descriptors for datasets of organic molecules, peptides and proteins using Bioclipse and R.
  • Introduction of software for statistical modeling, including R, LIBSVM, Weka and Bioclipse.
  • Hands-on exercises on supervised and unsupervised methods for statistical modeling/analysis, including use of PCA, PLS , SVM, SOM, random forest and k -NN with Weka.
  • Hands-on exercises in building of QSAR and proteochemometrics models with R: obtaining of dataset and pre-processing, model building, predictions and interpretations. Hands-on exercises of cluster analysis with R.
  • Hands-on exercises on statistical molecular design for optimization of lead compounds.
  • Eligibility*: At least 150 ECTS credits in chemistry, biology, biochemistry, pharmacy, medicine or dentistry. It is also required that you have passed the main course on Pharmaceutical Bioinformatic, 7.5 ECTS credits.

    Teaching: On the internet via a web-based teaching platform.

    Language: English.

    Examination: Written examination at the end of the course and approved compulsory modules.

    Credits: 5 ECTS credits is given on passing the course.

    Course literature: Provided online with the course and freely available computer programs that can be downloaded from the web and installed on your own computer.

    Course starts: The course is given twice a year; each spring and each autumn. Each course is running over eight weeks.

    Signing up: To sign up click here

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