Difference between revisions of "FND-STA-Information theory"
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| − | ... | + | After working through this unit you ... |
| + | * ... can calculate the informational entropy in a distribution of observed amino acids; | ||
| + | * ... are familar with various ways to define the informational entropy of reference distributions; | ||
| + | * ... can calculate information as the difference between expected and observed entropy. | ||
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Revision as of 11:35, 24 October 2017
Concepts of Information Theory
Keywords: Information theory
Contents
This unit is under development. There is some contents here but it is incomplete and/or may change significantly: links may lead to nowhere, the contents is likely going to be rearranged, and objectives, deliverables etc. may be incomplete or missing. Do not work with this material until it is updated to "live" status.
Abstract
A brief introduction to entropy and information: information theory appled to amino acid disributions.
This unit ...
Prerequisites
You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:
- Calculus: functions and equations; polynomial functions, logarithms, trigonometric functions; integrals and derivatives; theorem and proof.
- Geometry: length, area, volume; Euclidian and non-Euclidian space.
- Probability: event, probability, hypothesis and significance.
- Physical chemistry: Kinetics and equilibria, transition states, chemical reactions; enthalpy, entropy and free energy; acid-base equilibria, Boltzmann's law.
You need to complete the following units before beginning this one:
Objectives
This unit will ...
- ... introduce concepts of the foundations of information theory and its application to amino acid distributions.
Outcomes
After working through this unit you ...
- ... can calculate the informational entropy in a distribution of observed amino acids;
- ... are familar with various ways to define the informational entropy of reference distributions;
- ... can calculate information as the difference between expected and observed entropy.
Deliverables
- Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
- Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
- Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.
Evaluation
Evaluation: NA
- This unit is not evaluated for course marks.
Contents
Task:
- Read the introductory notes on concepts of Shannon's theory of information as applied in bioinformatics.
Further reading, links and resources
Notes
Self-evaluation
If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-08-05
Modified:
- 2017-10-23
Version:
- 1.0
Version history:
- 1.0 First live version
- 0.1 First stub
This copyrighted material is licensed under a Creative Commons Attribution 4.0 International License. Follow the link to learn more.
