Master’s studies

# Syllabus for Statistical Inference for Bioinformatics

Statistisk slutledning för bioinformatik

## Syllabus

• 5 credits
• Course code: 1MB459
• Education cycle: Second cycle
• Main field(s) of study and in-depth level: Technology A1N, Bioinformatics A1N
• Grading system: Fail (U), 3, 4, 5
• Established: 2017-03-07
• Established by: The Faculty Board of Science and Technology
• Revised: 2018-02-07
• Revised by: The Faculty Board of Science and Technology
• Applies from: week 30, 2018
• Entry requirements: Alt. 1) 120 credits in the engineering programme in molecular biotechnology including Multivariate data analysis and experimental design. Alt. 2) 120 credits. Bioinformatics  - starting course (can be read in parallel).

• Responsible department: Biology Education Centre

## Learning outcomes

After passing the course the student should be able to

• account for and apply classical statistical inference based on Bayesian and frequentist methods, traditional computer-based methods, as well as computer-intensive methods, for analysis one variable  alone and several variables at the same time
• choose and apply the appropriate among above-mentioned methods and technologies for statistical inference for a given set of biological and biomedical molecular data and their associated biomedical questions.

## Content

Classical inference: Frequentist and Bayesian inference, maximum likelihood estimation. Traditional computer-based methods: Empirical Bayes, ridge regression, generalized linear models, regression trees, survival analysis and the EM-algorithm. Computer-intensive methods as resampling, resampling based confidence intervals, cross validation, large-scale hypothesis testing, sparse regression models, random forests, and boosting. Bioinformatic application examples.

## Instruction

Lectures, calculation exercises and computer labs.

## Assessment

Written examination (3 credits), computer labs (2 credits).

## Syllabus Revisions

Applies from: week 30, 2018

• Good, Phillip I. Introduction to Statistics Through Resampling Methods and R, Second Edition

Wiley, 2013

Probably available as e-book through the Uppsala University Library

Find in the library

• Efron, Bradley; Hastie, Trevor Computer age statistical inference : algorithms, evidence, and data science

New York, NY: Cambridge University Press, 2016