150 credits, including 120 credits in biomedicine, pharmaceutical science, chemistry, pharmaceutical development, natural science and/or technology and 4 credits in Bioinformatics.
This course teaches how to solve practical problems in pharmacology, life sciences, chemistry and bioinformatics through predictive modelling. The course is a continuation of the course Pharmaceutical Bioinformatics. The focus of this course is practical applications and continuation studies on how to use predictive statistical modelling methods, primarily directed to applications in the pharmaceutical field. The course includes: Introduction to statistical modelling in pharmaceutical bioinformatics. Continuation on QSAR and proteochemometrics and descriptors of proteins, peptides and organic molecules. Supervised and unsupervised methods for statistical modelling/analysis, such as PCA, PLS, SVM, random forest and neural networks. Cluster analysis; methods and tools. A short introduction to the programming language Python. Practical exercises on cluster analysis. Practical exercises on the use of PCA, PLS, SVM, random forest and neural networks. Practical exercises in building QSAR and proteochemometrics models. Outline for distance course: The course is given as web lectures, through downloadable documents for self-studies and teacher-supervised exercises online through a web-based teaching platform, and through exercises that you solve on your own computer using software that can be downloaded from the net. The course has no fixed times and you can carry out your duties at any time of each study week. Communication between teacher and student is via an educational platform and via email. Written examination is performed online at the end of the course.
