Algorithms in Bioinformatics - #22125
Information for participants
GENERAL SCHEDULE
Lectures will be in the morning from 9.00 - 12.00, and exercises in the afternoon from 13.00 - 17.00.
The morning sessions will consist of lectures and small
practical exercises introducing the different algorithms, and the afternoon sessions
will consist of programming exercises where
the algorithms will be implemented.
The main programming language will be Python
and all program templates provided in the course will be written in Python.
Prior detailed knowlegde of Python programming is strongly encouraged but NOT required.
However, basic programming skills are required to follow the course.
PROGRAMS AND TOOLS
Course Programme
Please note that the programme is updated on a regular basis - click the 'refresh' button once in a while to make sure that you have the most updated information
LITERATURE:
-
The course curriculum consists of review paper and selected chapters from Immunological Bioinformatics,
Lund et al., MIT Press, 2005. All course material will be made available online during the course.
All course material is available either as open access or here Course material
Thursday, 6. June
Introduction to course. PSSM construction from pre-aligned sequences including pseudo count. Python recap
Morten Nielsen
BACKGROUND TEXTS
Essentials:
Additionals:
- 9.00 - 9.15
- Introduction to course
- Introduction to course [PDF]
(MP4).
- 9.15 - 9.30
- Introduction to the immune system [PDF] (MP4).
- 9.30 - 11.20 (coffee break included)
- Weight matrix construction[PDF]. [PPTX] Weight matrix construction (MP4).
- Logo Handout
- Handout. Estimation of pseudo counts
- 11.20 - 12.00
- Questions to the mornings lectures and other general issues
- Checking that we all have python and jupyter-notebook installed and running
- 12.00 - 13.00
- Lunch
- 13.00 - 13.30
- Some notes on sequence alignment [PDF] (MP4)
- 13.30 - 17.00
- A brief introduction to Python programming and Jupyter-notebooks
- Python intro
- Python Answers
- Implementation of PSSM construction from pre-aligned sequences including pseudo count
correction for low counts and sequence clustering
- PSSM construction and evaluation
Friday, 7. June
Sequence alignment, Dynamic programming, and Psi-Blast
Morten Nielsen
BACKGROUND TEXTS
Essentials:
Additionals:
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 11.00
- Blosum matrices [PDF] (MP4)
- Sequence alignment [PDF] . [PPTX] . (MP4)
- Handout (O3)
- Handout (O2)
- Handout answers
- 11.00 - 12.00
- Blast alignment heuristics, Psi-Blast, and sequence profiles [PDF] . [PPTX] .
- Psi-Blast handout.
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Implementation of the Smith-Waterman Dynamic
programming algorithm
- Matrix dumps from alignment programs (to be used for debugging)
Monday, 10. June.
Data redundancy reduction algortihms
Optimizations methods
Gibbs sampling
Morten Nielsen
BACKGROUND TEXTS - Data redundancy reduction and Gibbs sampling
Essentials:
Additionals:
BACKGROUND TEXTS - Hidden Markov Models
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 10.00
- Data redundancy reduction algorithms (Hobohm1 and Hobohm2) [PDF]. [PPTX]. (MP4).
- 10.00 - 10.45
- Optimization procedures - Gradient decent, Monte Carlo
- Optimization procedures [PDF] [PPTX] (MP4)
- GD handout
- 10.45 - 11.00
- Break
- 11.00 - 12.00
- Gibbs sampling and Gibbs clustering
- Gibbs sampling [PDF] . [PPTX] . (MP4).
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- 13.00 - 14.00
- Lunch
- 14.00 - 17.00 Hobohm exercise and Gibbs Sampling
- Hobohm redundancy reduction algorithms
- Implementating of a Gibbs sampling algorithm for prediction of MHC class II binding
Tuesday, 11. June
Hidden Markov Models
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 12.00 (with a break somewhere)
- Hidden Markov models
- Viterbi decoding, Forward/Backward algorithm, Posterior decoding, Baum-Welsh learning
- Profile Hidden Markov Models (see Hidden Markov model slides from yesterday)
- [PDF]. [PPTX] HMM (MP4)
- Viterbi Handout
- Forward Handout
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Implementation of Viterbi and posterior decoding.
- Hidden Markov exercises
Wednesday, 12. June
Cross validation and training of data driven prediction methods. Stabilization matrix method (SMM)
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 10.00
- Cross validation and training of data driven prediction methods
- [PDF] . [PPTX] . [MP4].
- 10.00 - 10.30
- Stabilization matrix method (SMM) background
- [PDF] . [PPTX] . [MP4].
- SMM handout
- 10.30 - 10.45
- Break
- 10.45 - 12.00
- Implementing and evaluating SMM algorithms using cross-validation
- 12.00 - 13.00
- Lunch
- 13.00 - 13.30
- Quiz with questions capturing essential parts of course up to now Quiz. We will go over the answers to the quiz tomorrow morning.
- Description of potential projects and formation of groups
- Project suggestions, and descriptions.
- Document for project signup.
- 14.00 - 17.00
- Continuation of exercise
Thursday, 13. June
Artificial neural networks. Sequence encoding, feedforward and backpropagation algorithm
Morten Nielsen
BACKGROUND TEXTS
- Immunological Bioinformatics. MIT Press. Chapter 4.
- Background
- Sequence encoding
- Feed forward algorithm
- Back-propagation and neural network training
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 10.30
- Artificial neural networks[PDF] . [PPTX] . Artificial neural networks Part 1 (MP4). Artificial neural networks Part 2 (MP4).
- Handout
- 10.30 - 10.40
- Break
- 10.40 - 12.00
- Network training - backpropagation
- Training of artificial neural networks [PDF] . [PPTX] . (MP4)..
- Handout
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Artificial neural networks (Feedforward and Backpropagation)
Friday, 14. June
An introduction to Deep neural network architectures
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 10.00
- Trick for ANN training [PDF].
- 10.00 - 10.30
- NNAlign, alignment using ANN's [PDF]
- 10.30 - 12.00
- Exercise: Constructing and training Deep ANN methods (Yat-Tsai Richie Wan && Yuchen Li)
- Pytorch Intro slides.pdf.
- Deep Learning using FFNN and CNNs (PPTX).
- code.tar.gz.
- data.tar.gz.
- 12.00 - 13.00
- Lunch
- 13.00 -17.00
- Exercise: Constructing and training Deep ANN methods (Yat-Tsai Richie Wan && Yuchen Li) cont.
Monday, 17. June
An introduction to Deep neural network architectures
Introduction to the project work
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.30
- Questions to yesterdays lectures and exercises
- 9.30 - 10.00
- What goes on inside a CNN? (Morten Nielsen) [PDF]
- 10.00 - 10.15
- Break
- 10.15 - 11.00
- Doing things in C - A few examples: Alignment and ANN training
- Some code examples
- 11.00 - 12.00
- Selection of projects, formation of project groups and start of project work Document for project signup.
- 12.00 - 13.00
- Lunch
- 13.00 - 16.00
- Work on project (on your own)
Tuesday 18. - Monday 24. June. Project work
- No lectures. Project work
- Projects must be submitted (in PDF format) via campusnet Tuesday 20. of June 11.59 (just before lunch)
Tuessday, 25. June, Exam
The written exam will be available at the course page
Wedensday 21th of June at 9.59, and need to be handed in
via DTU Learn Wedensday 21th of June before 12.00. The exam is open
book, meaning that you can seek information at the internet. Chatbots are NOT allowed.
You however cannot consult anyone, and plagiarism is
seriously condemned. The exam will take place in the usual class room building 358 class room 061.
A
Wednesday 26. June
Project evaluation
Each group has 25 minutes to present their project including 10 minutes for
questions. Note, only the group will be present for the presentation of
the individual projects.
The project presentations will take place in building 202 rooom R8208.
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