Course syllabus MIAX10016 - Applied Algorithms in Forensics and Bioinformatics (FI - WS 2019/2020)
|Faculty:||Faculty of Informatics|
|Course unit code:||MIAX10016|
Course unit title:
|Applied Algorithms in Forensics and Bioinformatics|
|Planned learning activities and teaching methods:|
Applied informatics 1-st semester, 2-nd year of study, 2-nd level of study.
Level of study:
Prerequisites for registration:
Acquiring of the Credit and accomplishment of the exam.
Particular grades from classification scale of the credit system are given on the basis of evaluation by points, which is dependent on the resulting grade of success by study of the subject as follows:
A = 94-100 points = 1
B = 86-93 points = 1.5
C = 76-85 points = 2
D = 66-75 points = 2.5
E = 56-65 points = 3
FX = 0-55 points = 4
|Learning outcomes of the course unit:|
The Contents of this subject is a follow up of the subject Applied algorithms in information and communications technologies. The goal is to motivate students to creative thinking and further study of algorithms. It is show that many concepts and basic algorithms have applications in seemingly distant areas such as for example information compression, forensic and bioinformatics. It is first highlighted that some basic tools and approaches from Information Theory, Decision Theory, Communications theory could be applied in forensic and bioinformatics. (Entropy, MAP and ML criterion, Ziv Lempelov algorithm.) Later the selected algorithms used in bioinformatics are studied in more detail. Particularly the algorithms are taught, which are used in DNA analysis in connection with replication and with circadian clock. The importance of the studied area was recently underlined by the fact that for the discovery of mechanisms controlling the circadian rhythm The Nobel Prize in Physiology or Medicine 2017 was awarded to Jeffrey C. Hall, Michael Rosbash and Michael W. Young.
1. Information measure, entropy and its application in forensic;
2. Decision theory, system of hypothesis, MAP and ML criterion;
3. Applied algorithms for lossless compression;
4. Universal Applied algorithms for lossless and lossy compression;
5. Applied algorithms for universal coding methods in Forensic and bioinformatics;
6. Sequences with structure, tree and convolutional codes;
7. Similarity of sequences analysis - Viterbi algorithm;
9. Algorithms applied for DNA analysis in connection with replication I;
10. Algorithms applied for DNA analysis in connection with replication II;
11. Circadian clock mechanism;
12. Algorithms applied for DNA analysis in connection with circadian clock mechanism.
Recommended or required reading:
Language of instruction:
|Required knowledge: Slovak and English language or only English language in case the study is provided in English|
|Assessed students in total: 64|
Name of lecturer(s):
prof. Ing. Peter Farkaš, DrSc. (examiner, instructor, lecturer, person responsible for course)
|Last modification:||25. 11. 2019|
Last modification made by Ján Lukáš on 11/25/2019.