Bilgisayar Müh. Bölüm Binası, 1. kat, oda 107 Tel.:+90 (224) 294 2792 email: gozcan at uludag.edu.tr
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Website:
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Objective of the Course:
To learn methods and algorithms for analyzing high-volume biological data / signals. To learn how to obtain results that have meaning in medical terms with these methods.
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Contribution of the Course to Professional Development
To understand biological databases, to be able to design the necessary algorithms to process these databases
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Learning Outcomes:
1
Students learn the algorithms which are used to analze high volüme biological data.;
2
Students learn to use probabilistic prediction methods to solve problems.;
3
In terms of medicine, students understand the benefits of the bioinformatics algorithms;
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Course Content:
Week
Theoretical
Practical
1
Fundamental problems of bioinformatics and its computations
2
Sequence Alignment Algorithms
3
Short read sequence alignment
4
Alignment against database, BLAST
5
Multiple Sequence Alignment
6
Motif search algorithms
7
Probabilistic algorithms
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Phylogeny Algorithms
9
Next Generation Sequencing
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Genomic Integration
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Biological networks
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Secondary prediction
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Protein structure prediction
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Interaction with cancer drugs
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Textbooks, References and/or Other Materials:
1. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998
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Assesment
TERM LEARNING ACTIVITIES
NUMBER
PERCENT
Midterm Exam
1
25
Quiz
0
0
Homeworks, Performances
2
15
Final Exam
1
60
Total
4
100
Contribution of Term (Year) Learning Activities to Success Grade
40
Contribution of Final Exam to Success Grade
60
Total
100
Measurement and Evaluation Techniques Used in the Course
Measurement and evaluation is carried out according to the priciples of Bursa uludag University Associate and Undergraduate Education Regulation.
Information
The relative evoluation system is applied. 1 Midterm and 1 Final exams are held.
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ECTS / WORK LOAD TABLE
Activites
NUMBER
TIME [Hour]
Total WorkLoad [Hour]
Theoretical
14
3
42
Practicals/Labs
0
0
0
Self Study and Preparation
0
0
0
Homeworks, Performances
2
0
0
Projects
0
0
0
Field Studies
0
0
0
Midtermexams
1
50
50
Others
0
0
0
Final Exams
1
58
58
Total WorkLoad
200
Total workload/ 30 hr
5
ECTS Credit of the Course
5
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CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
