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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
TDA507 - Computational methods in bioinformatics
Beräkningsmetoder inom bioinformatik
 
Syllabus adopted 2020-02-20 by Head of Programme (or corresponding)
Owner: MPDSC
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Bioengineering, Computer Science and Engineering, Software Engineering
Department: 37 - COMPUTER SCIENCE AND ENGINEERING


Teaching language: English
Application code: 87114
Open for exchange students: Yes
Block schedule: A

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0113 Written and oral assignments 7,5c Grading: TH   7,5c    

In programs

MPHPC HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
MPHPC HIGH-PERFORMANCE COMPUTER SYSTEMS, 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)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
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 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Graham Kemp

  Go to Course Homepage


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
Competence and skills
  • design and implement computational solutions to problems in bioinformatics
Judgement and approach
  • 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.


Page manager Published: Thu 04 Feb 2021.