Computer Algorithms in Bioinformatics (ETF RII RAB 51050 ) |
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General information |
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Module title | Computer Algorithms in Bioinformatics |
Module code | ETF RII RAB 51050 |
Study | ETF-B |
Department | Computing and Informatics |
Year | 2 |
Semester | 4 |
Module type | Elective |
ECTS | 4 |
Hours | 50 |
Lectures | 28 |
Exercises | 22 |
Tutorials | 0 |
Module goal - Knowledge and skill to be achieved by students |
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| Objective of this course is to improve the understanding of living systems through computer algorithms. Complexity of these systems offers challenges in software and algorithms, and often requires completely new approaches in computer science. Through this course students will learn to use WEB-biological databases, dedicated software packages and formats for search, analysis, modeling and simulation in the field of proteomics and genomics. | |
Syllabus |
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| 1. BIOINFORMATICS: definition of bioinformatics, tasks and bioinformatics objective, introduction to the basics of molecular biology, basic cell architecture, structure of DNA, genes and proteins, genome, proteome, transcripteome, the central dogma, Crick-Watson model. <br> 2. SOFTWARE RESOURCES: databases, data mining, computer associations with biological processes, software tools: Perl, Blasta, FASTA, PDBFIND databases, MATLAB Bioinformatics Toolbox. <br> 3. CLASSICAL METHODS AND ALGORITHMS IN BIOINFORMATICS: probabilistic approach, Bayes' theorem, HMM model, the nearest neighbor method, clustering method, identification trees method. <br> 4. BIOINSPIRED METHODS AND ALGORITHMS IN BIOINFORMATICS: neural networks, evolutionary algorithms, genetic algorithms, multi-target genetic algorithms. <br> 5. DNA SEQUENCE ANALYSIS: DNA sequence analysis, sequences pairing, pairing of multiple sequences, visualization sequences pairing, biological codes, sequence manipulation, statistics from the sequences, examples. <br> 6. MICROARRAY ANALYSIS: microarray normalization, microarray visualization, examples. <br> 7. ANALYSIS AND PREDICTION OF PROTEIN STRUCTURES: deterministic patterns, stochastic patterns, secondary structure prediction based on neural networks, visualization of protein structures. <br> |
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Literature |
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| Recommended | 1. Notes and slides from lectures (See Faculty WEB Site) <br> 2.Bioinformatics Computing, Bryan Bergeron, Prentice Hall PTR, 2002,ISBN:0-13-100825-0 <br> 3. Developing Bioinformatics Computer Skills,Cynthia Gibas, Per Jambeck, O'REILLY, 2001, ISBN: 1-56592-664-1 <br> 4. Begining Perl for Bioinformatics, James Tisdall, O'REILLY 2001, ISBN: 0-596-00080-4 <br> |
| Additional | 1. Bioinformatics Toolbox, The MathWorks, 2003 <br> 2. Vještačka inteligencija & fuzzy-neuro-genetika, Zikrija Avdagić, Grafoart <br> |
Didactic methods |
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| Through lectures, students will learn about the theory, tasks and applicative examples within thematic units. Lectures consist of theoretical part, presentational descriptive examples, genesis and resolution of specific tasks. In this way, students will have basis for appliance of skilled material in engineering applications. Additional examples and exam tasks are discussed and solved during the laboratory exercises. Laboratory practice and home assignments will enable students of continuous work and their knowledge verification. | |
Exams |
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| During the course students will collect points according to the following system: <br> - Attending lectures, exercises and tutorial classes: 10 points, student with more then three absences from lectures, exercises and/or tutorials can not achieve these points; <br> - Home assignments: maximum of 10 points, assuming solving 5 to 10 assignments evenly distributed throughout the semester; <br> - Partial exams: two written partial exams, maximum of 20 points for each positively evaluated partial exam; <br> Student who during the semester achieved less than 20 points must re-enroll this course. <br> Student who during the semester achieved 40 or more points will access to final oral exam, the exam consists of discussing the partial exams tasks, home assignments and answers to simple questions related to course topics. <br> Final oral exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum will access to makeup oral exam. <br> Student who during the semester achieved 20 or more points, and less than 40 points will access to makeup exam. Makeup exam is structured as follows: <br> - Written part structured in the same way as a partial written exam, during which students solve problems in topics they failed on partial exams (achieved less then 10 points), <br> - Oral part structured in the same way as a final oral exam. <br> Only students who, after passing the written part of the makeup exam managed to achieve a total score of 40 or more points, can access to oral makeup exam, where the score consists of points achieved through attending classes, home assignments, passing partial exams and passing the written part of makeup exam. <br> Oral makeup exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum must re-enroll this course. <br> |
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Aditional notes |
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| Tools and software packages that will be used during the exercises and for home assignments: MATLAB, Bioinformatic Toolbox, Statistic Toolbox, web-tools. | |