SGN53007 Computational Diagnostics, 5 cr
Additional information
Suitable for postgraduate studies
Person responsible
Frank EmmertStreib
Lessons
Implementation 1: SGN53007 201501
Study type  P1  P2  P3  P4  Summer 






Requirements
To complete the course, the student is required to (all three requirements must be completed to pass the course):
a) Execute the project work (20% of the final grade)
b) Execute the weekly exercises (1 per exercises lesson, 40% of the final grade)
c) Do the final exam (40% of the final grade)
Completion parts must belong to the same implementation
Learning Outcomes
After completing the course, the student gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for biomedical research. Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to largescale and highdimensional data sets from genomic and genetic experiments. Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. In general, the course teaches statistical thinking in the context of biomedical problems, i.e., the adaptation of machine learning methods in a problem specific manner.
Content
Content  Core content  Complementary knowledge  Specialist knowledge 
1.  Classification of disease groups  Computational implementation and interpretation; classification methods  
2.  Biomarker identification  Feature selection methods  
3.  Survival analysis  Regression models for timeto event processes  
4.  Genomics data  Preprocessing and normalization of gene expression data from microarray experiments  
5.  Programming in R  Usage and programming in the statistical programming language R  
6.  Quantitative assessment of results  Statistical error measures; resampling techniques  
7.  Predictive models  Linear regression, hypothesis testing; general models in data science 
Instructions for students on how to achieve the learning outcomes
To complete the course, the student is required to (all three requirements must be completed to pass the course): a) Execute the project work (20% of the final grade) b) Execute the weekly exercises (1 per exercises lesson, 40% of the final grade) c) Do the final exam (40% of the final grade)
Assessment scale:
Numerical evaluation scale (15) will be used on the course
Partial passing:
Study material
Type  Name  Author  ISBN  URL  Additional information  Examination material 
Book  An Introduction to Statistical Learning  Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani  Introductory overview of many methods discussed in the lectures.  No  
Book  Statistics and Data Analysis for Microarrays Using R and Bioconductor  Sorin Drăghici  Introduction to the analysis of microarray data.  No 
Additional information about prerequisites
Basic programming skills. Experience with the language R are desirable, but not necessary. Basic knowledge in Mathematics and Machine Learning. Basic knowledge of biology/systems biology.
Correspondence of content
Course  Corresponds course  Description 
SGN53007 Computational Diagnostics, 5 cr  SGN53006 Computational Modeling in Biomedical Problems, 5 cr 