SCHOOL

Engineering

ACADEMIC UNIT

Department of Electrical and Computer Engineering

LEVEL OF STUDIES

7

COURSE CODE

ECE_AK807

SEMESTER

8

COURSE TITLE

INTRODUCTION TO BIOINFORMATICS

INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

WEEKLY TEACHING HOURS

CREDITS

Lectures

2

2

Recitation sections

2

2

Project

1

1

Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

5

5

COURSE TYPE

general background,
special background, specialised general knowledge, skills development

Specialised general knowledge and skills development.

PREREQUISITE COURSES:

Recommended prerequisite knowledge are the courses of data structures (CEID_NE233),  algorithms (CEID_NY205) and data base development  (CEID_NY334) .

LANGUAGE OF INSTRUCTION and EXAMINATIONS:

Greek.  Instruction may be given in English if foreign students attend course.

IS THE COURSE OFFERED TO ERASMUS STUDENTS

Yes.

COURSE WEBSITE (URL)

https://www.ceid.upatras.gr/webpages/courses/cplusplus/bioinfo/index.htm

https://eclass.upatras.gr/courses/CEID1047/

Learning outcomes

The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·    Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·    Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·    Guidelines for writing Learning Outcomes

Learning outcomes:

At the end of this course the student should be able to:

1.       present the main principles and notions of Bioinformatics,

2.       understand the connection that exists between the problems in managing biological macromolecules, and techniques for managing strings

3.       design and implement algorithms for string processing in order to solve biological problems,  

4.       understand the basic principles of computer aided drug design,   

5.       present the main clustering and classification algorithms as they are applied in order to solve biological problems.

At the end of the course the student will have further developed the following skills/competences:

1.      ability to exhibit knowledge and understanding of the basic principles and notions of bioinformatics and string processing algorithms

2.      ability to apply methodologically this knowledge and understanding in order to solve problems in bioinformatics

3.      ability to cooperate with others in order to solve complex problems in the area of bioinformatics

General Competences

Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

……

Others…

…….

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Decision-making

Working independently

Team work

Project planning and management

Production of free, creative and inductive thinking

Production of new research ideas

First Part

Introduction  to algorithms for efficient management and storage of  strings and sequences of biological data.

Algorithms for exact pattern matching  (Boyer-Moore,Knuth-Morris-Pratt,Shift-Or,  Multiple Pattern Matching).

Introduction to the suffix tree and its applications

Algorithms for approximate pattern matching and string/sequence alignment .

Algorithms for searching data base sequences  (FASTA, BLAST, PROSITE)

Second Part

The theoretical  base of Molecular Design

Molecular Models and Biochemical Information

Structure Based Drug Deign

Open problems

Third Part

Techniques for biological data classification and clustering targeting at predicting the behavior of biological molecules.

DELIVERY
Face-to-face, Distance learning, etc.

Face-to-face

USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Use of ICT in teaching, laboratory education, communication with students

We use  Information and Communications Technology in communicate with students. We use e_class, e_mail and forum

TEACHING METHODS

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

STUDENT PERFORMANCE EVALUATION

Description of the evaluation procedure

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

(1) delivering and solving a set of exercises in string processing topics (30% of the total grade) ,

(2) composing and presenting an essay by groups of 1-2 students, concerning the critical presentation and analysis of a set of scientific papers that deals with a specific topic of the course, and an oral examination on the notes of the course (70% of the final grade)

- Suggested bibliography:

- Related academic journals:

1.     Neil Jones, Pavel Pevzner, An Introduction to Bioinformatics Algorithms, MIT 2004.

2.     Σοφία Κοσσίδα "Βιοπληροφορική, Δυνατότητες και Προοπτικές",  Εκδόσεις Νέων Τεχνολογιών Μον. ΕΠΕ 2009

3.     Dan Gusfield, Algorithms on Strings, Trees, and Sequences -  Computer Science and Computational Biology, Cambridge University Press,  1997.

4.     S. Aluru, Handbook of Computational Molecular Biology , Chapman & All/Crc Computer and Information Science Series, 2006.