C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2025
- Extent and Intensity
- 1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- RNDr. Tomáš Raček, Ph.D. (lecturer)
- Guaranteed by
- RNDr. Tomáš Raček, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 0/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50 - fields of study / plans the course is directly associated with
- Bioinformatics (programme PřF, B-BIC)
- Epidemiology and modeling (programme PřF, B-MBB)
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Listed among pre-requisites of other courses
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2024
- Extent and Intensity
- 1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- RNDr. Tomáš Raček, Ph.D. (lecturer)
- Guaranteed by
- RNDr. Tomáš Raček, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 19. 2. to Sun 26. 5. Mon 9:00–9:50 B11/306
- Timetable of Seminar Groups:
C2142/02: Mon 19. 2. to Sun 26. 5. Tue 14:00–15:50 C04/211, T. Raček - Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 22/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50 - fields of study / plans the course is directly associated with
- Bioinformatics (programme PřF, B-BIC)
- Epidemiology and modeling (programme PřF, B-MBB)
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2023
- Extent and Intensity
- 1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- RNDr. Tomáš Raček, Ph.D. (lecturer)
doc. RNDr. Radka Svobodová, Ph.D. (lecturer) - Guaranteed by
- doc. RNDr. Radka Svobodová, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 12:00–12:50 B11/205
- Timetable of Seminar Groups:
C2142/02: Tue 14:00–15:50 C04/211, T. Raček - Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 9/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50 - fields of study / plans the course is directly associated with
- Bioinformatics (programme PřF, B-BIC)
- Epidemiology and modeling (programme PřF, B-MBB)
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2022
- Extent and Intensity
- 1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- RNDr. Tomáš Raček, Ph.D. (lecturer)
doc. RNDr. Radka Svobodová, Ph.D. (lecturer) - Guaranteed by
- doc. RNDr. Radka Svobodová, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 18:00–18:50 B11/205
- Timetable of Seminar Groups:
C2142/02: Tue 14:00–15:50 C04/211, T. Raček - Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 5/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50 - fields of study / plans the course is directly associated with
- Bioinformatics (programme PřF, B-BIC)
- Epidemiology and modeling (programme PřF, B-MBB)
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2021
- Extent and Intensity
- 1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- doc. RNDr. Radka Svobodová, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 1. 3. to Fri 14. 5. Tue 13:00–13:50 online_BCH1, Thu 13:00–14:50 online_BCH1
- Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 0/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50 - fields of study / plans the course is directly associated with
- Bioinformatics (programme PřF, B-BIC)
- Epidemiology and modeling (programme PřF, B-MBB)
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2020
- Extent and Intensity
- 1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- doc. RNDr. Radka Svobodová, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Tue 12:00–12:50 C04/211, Thu 14:00–15:50 C04/211
- Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 1/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20 - fields of study / plans the course is directly associated with
- Bioinformatics (programme PřF, B-BIC)
- Epidemiology and modeling (programme PřF, B-MBB)
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2019
- Extent and Intensity
- 1/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Zdeněk Glatz, CSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 18. 2. to Fri 17. 5. Tue 12:00–12:50 C04/211, Thu 14:00–15:50 C04/211
- Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20 - fields of study / plans the course is directly associated with
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
- Teacher's information
- http://www.fi.muni.cz/~xracek/c2142_spring2015/index.html
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2017
- Extent and Intensity
- 1/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Zdeněk Glatz, CSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 20. 2. to Mon 22. 5. Tue 12:00–12:50 C04/211
- Timetable of Seminar Groups:
- Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 60 student(s).
Current registration and enrolment status: enrolled: 0/60, only registered: 0/60, only registered with preference (fields directly associated with the programme): 0/60 - fields of study / plans the course is directly associated with
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
- Learning outcomes
- At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
- Teacher's information
- http://www.fi.muni.cz/~xracek/c2142_spring2015/index.html
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2016
- Extent and Intensity
- 1/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Zdeněk Glatz, CSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 11:00–11:50 C04/211
- Timetable of Seminar Groups:
- Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 60 student(s).
Current registration and enrolment status: enrolled: 0/60, only registered: 0/60, only registered with preference (fields directly associated with the programme): 0/60 - fields of study / plans the course is directly associated with
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
- Teacher's information
- http://www.fi.muni.cz/~xracek/c2142_spring2015/index.html
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2015
- Extent and Intensity
- 1/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Zdeněk Glatz, CSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Tue 12:00–12:50 B11/205
- Timetable of Seminar Groups:
C2142/02: Thu 14:00–15:50 C04/211, T. Raček - Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 60 student(s).
Current registration and enrolment status: enrolled: 0/60, only registered: 0/60, only registered with preference (fields directly associated with the programme): 0/60 - fields of study / plans the course is directly associated with
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford, Floyd-Warshall). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal). Application in processing molecular graph.
- 10. Approaches to solving problems (brute force, greedy algorithms, divide and conquer). Application in chemoinformatics and bioinformatics.
- 11. Hard problems (TSP, SAT, P vs. NP). Examples of life science hard problems.
- 12. Advanced data structures (AVL, B+ trees, Union-Find). Application in biology and chemistry.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
- Teacher's information
- http://www.fi.muni.cz/~xracek/c2142_spring2015/index.html
C2142 Design of algorithms in life sciences
Faculty of ScienceSpring 2014
- Extent and Intensity
- 1/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer)
Mgr. Tomáš Bouchal, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Zdeněk Glatz, CSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Mon 16:00–16:50 B11/205
- Timetable of Seminar Groups:
C2142/02: Tue 11:00–12:50 C04/118, T. Raček
C2142/03: Thu 12:00–13:50 C04/118, T. Raček - Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 60 student(s).
Current registration and enrolment status: enrolled: 0/60, only registered: 0/60, only registered with preference (fields directly associated with the programme): 0/60 - fields of study / plans the course is directly associated with
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford, Floyd-Warshall). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal). Application in processing molecular graph.
- 10. Approaches to solving problems (brute force, greedy algorithms, divide and conquer). Application in chemoinformatics and bioinformatics.
- 11. Hard problems (TSP, SAT, P vs. NP). Examples of life science hard problems.
- 12. Advanced data structures (AVL, B+ trees, Union-Find). Application in biology and chemistry.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
- Teacher's information
- http://www.fi.muni.cz/~xracek/c2142_spring2014/index.html
C2142 Design of algorithms in life sciences
Faculty of Sciencespring 2018
The course is not taught in spring 2018
- Extent and Intensity
- 1/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Zdeněk Glatz, CSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Timetable
- Tue 12:00–12:50 C04/211, Thu 14:00–15:50 C04/211
- Prerequisites
- Basic familiarity with an arbitrary programming language is an advantage, but it's not required. Programming examples are written in Python.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 60 student(s).
Current registration and enrolment status: enrolled: 0/60, only registered: 0/60, only registered with preference (fields directly associated with the programme): 0/60 - fields of study / plans the course is directly associated with
- Chemoinformatics and Bioinformatics (programme PřF, N-BCH)
- Course objectives
- Introduce the fundamental principles for efficient algorithms and data structures design demonstrated on interesting science examples.
At the end of the course the students will be able to describe and to use known algorithms for solving common problems.
Moreover, they will be able to design new approaches to the particular applications with an emphasis on efficiency of the solution.
The graduates will also have the ability to critically evaluate and choose optimal solution to the uncommon problems. - Syllabus
- 1. From problem to algorithm. Specification, correctness.
- 2. Introduction to complexity. Examples of life science problems with logarithmic, polynomial and exponential complexity.
- 3. Basic data structures (linked list, queue, stack). Applications in biology and chemistry.
- 4. Motivation to the data searching, sorting algorithms (binary search, Selection sort, Merge sort). Applications in processing chemoinformatics and bioinformatics data.
- 5. Search trees, heaps (BST, Heap sort). Applications in processing chemoinformatics and bioinformatics data.
- 6. Hashes, indices. Possibilities of use in biology and computer chemistry.
- 7. Basic graph theory terms, graph representation, methods of traversal (BFS, DFS). Molecular graph.
- 8. Shortest path problem (Dijkstra, Bellman-Ford). Application in working with molecular graph.
- 9. Spanning trees (Prim, Kruskal, Union-Find). Application in processing molecular graph.
- 10. Approaches to solving problems I (brute force, recursive algorithms, divide and conquer, greedy algorithms). Application in chemoinformatics and bioinformatics.
- 11. Approaches to solving problems II (backtracking, dynamic programming, heuristics). Applications in biology and chemistry.
- 12. Hard problems (TSP, P vs. NP). Examples of life science hard problems.
- Literature
- recommended literature
- Introduction to algorithms. Edited by Thomas H. Cormen. 3rd ed. Cambridge, Mass.: MIT Press, 2009, 1 online r. ISBN 0262533057. info
- COMPEAU, Phillip and Pavel PEVZNER. Bioinformatics algorithms : an active learning approach. La Jolla, Calif.: Active Learning Publishers, 2014, xxii, 362. ISBN 9780990374602. info
- Teaching methods
- Lectures supplemented by seminars. Optional homework.
- Assessment methods
- Final written exam. For successful completion of the course at least 60 % of points is required for examination and 40 % for credit.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Listed among pre-requisites of other courses
- Teacher's information
- http://www.fi.muni.cz/~xracek/c2142_spring2015/index.html
- Enrolment Statistics (Spring 2025, recent)