Difference between revisions of "FND-STA-Information theory"

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<div style="padding:5px; border:1px solid #000000; background-color:#b3dbce; font-size:300%; font-weight:400; color: #000000; width:100%;">
 
Concepts of Information Theory
 
Concepts of Information Theory
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<div style="padding:5px; margin-top:20px; margin-bottom:10px; background-color:#b3dbce; font-size:30%; font-weight:200; color: #000000; ">
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(Information theory)
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  {{Vspace}}
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</div>
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<div class="keywords">
 
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<b>Keywords:</b>&nbsp;
 
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Information theory, statistical mechanics, Boltzmann's law, statistical free energy
 
 
</div>
 
</div>
  
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{{Smallvspace}}
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__TOC__
 
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{{STUB}}
 
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{{Vspace}}
 
  
  
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<div style="padding:5px; border:1px solid #000000; background-color:#b3dbce33; font-size:85%;">
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<div style="font-size:118%;">
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<b>Abstract:</b><br />
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<section begin=abstract />
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A brief introduction to entropy and information: information theory appled to amino acid disributions.
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<section end=abstract />
 
</div>
 
</div>
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<div id="ABC-unit-framework">
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<!-- ============================  -->
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== Abstract ==
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<hr>
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "abstract" -->
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<table>
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... with perspectives on statistical mechanics, Boltzmann's law and the calculation of statistical "free energy".
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<tr>
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<td style="padding:10px;">
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{{Vspace}}
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<b>Objectives:</b><br />
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This unit will ...
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* ... introduce concepts of the foundations of information theory and its application to amino acid distributions.
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== This unit ... ==
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</td>
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=== Prerequisites ===
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<td style="padding:10px;">
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "prerequisites" -->
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<b>Outcomes:</b><br />
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<!-- included from "ABC-unit_components.wtxt", section: "notes-external_prerequisites" -->
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After working through this unit you ...
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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:
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* ... can calculate the informational entropy in a distribution of observed amino acids;
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<!-- included from "FND-prerequisites.wtxt", section: "calculus" -->
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* ... are familar with various ways to define the informational entropy of reference distributions;
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* ... can calculate information as the difference between expected and observed entropy.
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</td>
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</tr>
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</table>
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<!-- ============================ -->
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<hr>
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<b>Deliverables:</b><br />
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<section begin=deliverables />
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<li><b>Time management</b>: 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.</li>
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<li><b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.</li>
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<li><b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].</li>
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<section end=deliverables />
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<!-- ============================  -->
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<hr>
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<section begin=prerequisites />
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<b>Prerequisites:</b><br />
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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:<br />
 
*<b>Calculus</b>: functions and equations; polynomial functions, logarithms, trigonometric functions; integrals and derivatives; theorem and proof.
 
*<b>Calculus</b>: functions and equations; polynomial functions, logarithms, trigonometric functions; integrals and derivatives; theorem and proof.
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<!-- included from "FND-prerequisites.wtxt", section: "geometry" -->
 
 
*<b>Geometry</b>: length, area, volume; Euclidian and non-Euclidian space.
 
*<b>Geometry</b>: length, area, volume; Euclidian and non-Euclidian space.
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<!-- included from "FND-prerequisites.wtxt", section: "probability" -->
 
 
*<b>Probability</b>: event, probability, hypothesis and significance.
 
*<b>Probability</b>: event, probability, hypothesis and significance.
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<!-- included from "FND-prerequisites.wtxt", section: "physchem" -->
 
 
*<b>Physical chemistry</b>: Kinetics and equilibria, transition states, chemical reactions; enthalpy, entropy and free energy; acid-base equilibria, Boltzmann's law.
 
*<b>Physical chemistry</b>: Kinetics and equilibria, transition states, chemical reactions; enthalpy, entropy and free energy; acid-base equilibria, Boltzmann's law.
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<!-- included from "ABC-unit_components.wtxt", section: "notes-prerequisites" -->
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This unit builds on material covered in the following prerequisite units:<br />
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You need to complete the following units before beginning this one:
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*[[BIN-Sequence|BIN-Sequence (Sequence)]]
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*[[BIN-Sequence]]
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<section end=prerequisites />
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<!-- ============================  -->
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</div>
  
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{{Smallvspace}}
  
  
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=== Objectives ===
 
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "objectives" -->
 
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...
 
  
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{{Smallvspace}}
  
  
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=== Outcomes ===
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__TOC__
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "outcomes" -->
 
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...
 
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{{Vspace}}
 
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=== Deliverables ===
 
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "deliverables" -->
 
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<!-- included from "ABC-unit_components.wtxt", section: "deliverables-time_management" -->
 
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*<b>Time management</b>: 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.
 
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<!-- included from "ABC-unit_components.wtxt", section: "deliverables-journal" -->
 
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*<b>Journal</b>: Document your progress in your [[FND-Journal|course journal]].
 
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<!-- included from "ABC-unit_components.wtxt", section: "deliverables-insights" -->
 
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*<b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|insights! page]].
 
  
 
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=== Evaluation ===
 
=== Evaluation ===
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "evaluation" -->
 
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<!-- included from "ABC-unit_components.wtxt", section: "eval-none" -->
 
 
<b>Evaluation: NA</b><br />
 
<b>Evaluation: NA</b><br />
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:This unit is not evaluated for course marks.
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<div style="margin-left: 2rem;">This unit is not evaluated for course marks.</div>
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</div>
 
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<div id="BIO">
 
 
== Contents ==
 
== Contents ==
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "contents" -->
 
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...
 
  
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{{Task|1=
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* Read the introductory notes on {{ABC-PDF|FND-STA-Information_theory|concepts of Shannon's theory of information}} as applied in bioinformatics.
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}}
  
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== Further reading, links and resources ==
 
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<!-- {{#pmid: 19957275}} -->
 
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<!-- <div class="reference-box">[http://www.ncbi.nlm.nih.gov]</div> -->
 
  
 
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== Notes ==
 
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "notes" -->
 
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<references />
 
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== Self-evaluation ==
 
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<!-- included from "../components/FND-Information_theory.components.wtxt", section: "self-evaluation" -->
 
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=== Question 1===
 
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Question ...
 
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Answer ...
 
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<b>If in doubt, ask!</b> 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.
 
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:2017-08-05
 
:2017-08-05
 
<b>Modified:</b><br />
 
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:2020-09-24
 
<b>Version:</b><br />
 
<b>Version:</b><br />
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:0.1
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:1.1
 
<b>Version history:</b><br />
 
<b>Version history:</b><br />
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*1.1 2020 Maintenance
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*1.0 First live version
 
*0.1 First stub
 
*0.1 First stub
 
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Latest revision as of 11:40, 24 September 2020

Concepts of Information Theory

(Information theory)


 


Abstract:

A brief introduction to entropy and information: information theory appled to amino acid disributions.

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.

    • 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.

    This unit builds on material covered in the following prerequisite units:


     



     



     


    Evaluation

    Evaluation: NA

    This unit is not evaluated for course marks.

    Contents

    Task:


     


    About ...
     
    Author:

    Boris Steipe <boris.steipe@utoronto.ca>

    Created:

    2017-08-05

    Modified:

    2020-09-24

    Version:

    1.1

    Version history:

    • 1.1 2020 Maintenance
    • 1.0 First live version
    • 0.1 First stub

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