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
  • Applies from: week 27, 2017
  • 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 exercises.
 

Assessment

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

Reading list

Applies from: week 27, 2017

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

    New York, NY: Cambridge University Press, 2016

    Find in the library