Life Science Data Analytics
and Algorithmic Bioinformatics
Teaching

Lecture: Algorithmic Basics of Bioinformatics (WS, former course name: Algorithmic Bioinformatics I)

Location: B-IT

Time: Fr, 9:00 - 10:30, Hörsaal (21 and 28 October in Rheinsaal)

Exercises (obligatory):

Prerequisites: Bridging course in mathematics and computer science

Content: This mandatory lecture gives a broad overview about graph mining and sequence based algorithms, which are of high relevance in bioinformatics. The goal is to understand these methods, partially formulate them in pseudo-code and/or a programming language and to learn, how to correctly apply them. More detailed, the lecture covers the following topics:

  • Introduction
    • Motivation & Introduction to Bioinformatics
    • Algorithmic Prerequisites
  • Graph Theory & Network Medicine
  • Sequence Comparison
    • Dynamic Programming
    • Alignment algorithms
    • BLAST
  • Pattern Matching via Suffix Trees
  • DNA Sequencing and NGS Read Mapping

Prerequisites for getting credits are:

  • regular attendance of tutorials
  • at least N-1 homeworks submitted
  • at least N-2 programming tasks submitted
  • at least one group presenation of homeworks
  • >= 50% of points from all homework assignments
  • passing of exam

Literature:

  • A. Lesk, Introduction to Bioinformatics, Oxford University Press, 2008
  • N. Jones, P. Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004
  • D. Gusfield, Algorithms on Strings, Trees and Sequences, Cambridge University Press, 1997
  • P. Pevzner, Computational Molecular Biology - An Algorithmic Approach, MIT Press, 2001
  • M. Waterman, Introduction to Computational Biology, Chapman & Hall/CRC, 1995

Lecture: Data Mining and Machine Learning in Bioinformatics (SS, former course name: Algorithmic Bioinformatics II)

Location: B-IT

Time: Fr, 9:00 - 10:30, Hörsaal

Exercises (obligatory): Mo, 11:00 - 12:30, Marschallsaal

Prerequisites: Algorithmic Bioinformatics I, Bachelor Computer Science or equivalent qualification

Content: This lecture gives a broad overview about frequently used data mining and machine learning algorithms. The use of the respective methods to solve problems in bioinformatics is explained. The goal is to understand the explained methods, being able to apply them correctly and partially implement them. More detailed, the  the following topics are covered:

  • Introduction
  • Statististical Basics
    • Probability distributions and Bayesian inference
    • Statistical hypothesis testing
    • Linear models
    • Logistic regression
    • Principal Component Analysis
  • Data Mining and Machine Learning Algorithms
    • Clustering
    • Hidden Markov Models
    • Supervised Machine Learning
    • Support Vector Machines

Prerequisites for getting credits are:

  • regular attendance of tutorials (missing at most 2 times allowed without medical certificate)
  • at least one group presentation of homework solutions
  • at least N-1 homeworks submitted
  • at least N-1 programming tasks submitted
  • >= 50% of points from all homework assignments
  • passing of exam

Literature:

  • N. Jones, P. Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004
  • T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, 2008
  • S.Boslaugh, P. Watters, Statistics in a Nutshell, O'Reilly, 2008