Computational Biology of Infection Research

The Department of “Computational Biology for Infection Research” studies the human microbiome, viral and bacterial pathogens, and human cell lineages within individual patients by analysis of large-scale biological and epidemiological data sets with computational techniques. Focusing on high throughput meta’omics, population genomic and single cell sequencing data, we produce testable hypotheses, such as sets of key sites or relevant genes associated with the presence of a disease, of antibiotic resistance or pathogenic evasion of immune defense. We interact with experimental collaborators to verify our findings and to promote their translation into medical treatment or diagnosis procedures. To achieve its research goals, the department also develops novel algorithms and software.

Leader

Seminar: Statistical Learning with Application in R

The seminar "Statistical Learning with Application in R" is hosted by the department "Computational Biology of Infection Research" at the HZI headed by Prof. Alice McHardy.

Kick-off Meeting: 21st of March 2017, at 4 pm s.t

Room Kick-off Meeting: Room 045 (BRICS)

Date: one day at the end of the semester break

Room seminar: tba

Max. number of participants: 9

Language: English

Modus: 30 minutes of presentation + 10 minutes of discussion + 3-5 pages summary Designated for Master Students of Computer Science

In case you have questions about the seminar, feel free to contact Thorsten Klingen.

Description:  This course is designed to teach the basic principles of statistical learning. We will discuss statistical learning techniques that are commonly used in bioinformatics and computer science for data clustering, classification and analysis. These techniques are applied to solve biological problems, e.g. in genomics, systems biology, and evolution and are useful to draw clear insights from your data. Further, the students can test statistical methods using basic commands in R, which is a powerful software environment for statistical computing, analysis and graphics.

Statistical Learning

1)    Introduction to Statistical Learning
2)    Linear Regression
3)    Classification
4)    Resampling Methods
5)    Linear Model Selection
6)    Non-linear Modeling
7)    Tree-Based Methods
8)    Support Vector Machines
9)    Unsupervised Learning

Literature:

“An Introduction to Statistical Learning with Application in R” by James, Witten, Hastie and Tibshirani

”The Elements in Statistical Learning“ by Friedman, Tibshirani, and Hastie

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