Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameBeräkningsmetoder inom bioinformatik
- CodeTDA507
- Credits7.5 Credits
- OwnerMPDSC
- Education cycleSecond-cycle
- Main field of studyBioengineering, Computer Science and Engineering, Software Engineering
- DepartmentCOMPUTER SCIENCE AND ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 87111
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0113 Written and oral assignments 7.5 c Grading: TH | 7.5 c |
In programmes
- MPALG - COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (elective)
- MPALG - COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 2 (elective)
- MPCAS - COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
- MPCAS - COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
- MPCSN - COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
- MPCSN - COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 2 (elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
- MPHPC - HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
- MPHPC - HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 2 (elective)
- TKITE - SOFTWARE ENGINEERING, Year 3 (elective)
Examiner
Graham Kemp- Professor, Data Science and AI, Computer Science and Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
To be eligible for the course the student should have successfully completed 90 hec of studies within the subject Computer Science or equivalent. Furthermore, the student should have successfully completed a course in Programming and in Discrete Mathematics.Aim
This course demonstrates how computational methods that have possibly been presented in other computing courses can be applied to solve problems in an application area.We look at problems related to the analysis of biological sequence data (sequence bioinformatics) and macromolecular structures (structural bioinformatics). Computing scientists need to be able to understand problems that originate in areas that may be unfamiliar to them, and to identify computational methods and approaches that can be used to solve them. Biological concepts needed to understand the problems will be introduced.
This is an advanced level course which uses research articles as the main reference materials. Reading research articles is valuable training for scientists and researchers.
These demonstrate how to present ideas and methods, and how to critically evaluate them. Developing skill in reading research articles is useful preparation for future scientific investigations, and one's own scientific writing can improve through reading.
Learning outcomes (after completion of the course the student should be able to)
Knowledge and understanding- describe and summarise problems that have been addressed in the bioinformatics literature, and computational approaches to solving them
- design and implement computational solutions to problems in bioinformatics
- critically discuss different bioinformatics methods that address the same task or related tasks, and to discuss differences in the tasks addressed, or differences in the computational approaches
- identify situations where the same computational methods are applied in addressing different problems, even across different application areas
Content
Computational methods and concepts featured in this course include: dynamic programming; heuristic algorithms; graph partitioning; image skeletonisation, smoothing and edge detection; clustering; sub-matrix matching; geometric hashing; constraint logic programming; Monte Carlo optimisation; simulated annealing; self-avoiding walks.Biological problems featured in this course include: sequence alignment; domain assignment; structure comparison; comparative modelling; protein folding; fold recognition; finding channels; molecular docking; protein design.
Organisation
Lectures and programming assignments.Literature
Lecture handouts; web-based resources; selected research articles.Examination including compulsory elements
The course is examined by individual programming assignments and written assignments.
The grading scale comprises 3, 4, 5 and Fail (U).
The lowest pass grade reflects fulfilment of the learning outcomes demonstrated by satisfactory completion of the assignments. A higher grade requires a greater level
of understanding, insight and reflection.
To pass the course, the assignments must pass. To get a higher grade, a higher weighted average from the grades of the assignments is required.
The lowest pass grade reflects fulfilment of the learning outcomes demonstrated by satisfactory completion of the assignments. A higher grade requires a greater level
of understanding, insight and reflection.
To pass the course, the assignments must pass. To get a higher grade, a higher weighted average from the grades of the assignments is required.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.