• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Use the most recent methods and tool in biyoinformatics and systems biology research

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.Question and find out innovative approaches

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Propose new ideas to be able to improve the existing tools and methods

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.Question and find out innovative approaches
   3. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Use their newly acquired skills in their own research projects

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.Question and find out innovative approaches
   3. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Data normalization and imputation methods for NGS data Homework1
Week 2:	Variant analysis in cancer and rare diseases Homework2
Week 3:	ML based methods for variant analysis and classification Homework3
Week 4:	Modelling error patterns in NGS data Homework4
Week 5:	TCGA data and its utilization in cancer studies Homework5
Week 6:	IIntegration of genome and RNAseq in clinics Homework6
Week 7:	Data analysis is spatial transcriptomics Homework7
Week 8:	3D tissue modelling using PhysiCell Homework8
Week 9:	ML/DL based methods for modelling and analysis of scRNA data Homework9
Week 10:	Stochastic modelling / simulations in kinetic modelling Homework10
Week 11:	Drug target identification through genome-scale metabolic modeling Homework11
Week 12:	Machine learning in genome scale metabolic modelling Homework12
Week 13:	Advanced algorithms on integration of transcriptome data with genome scale metabolic networks Homework13
Week 14:	Algorithms on integration of metabolome data with genome scale metabolic networks Homework14
Week 15*:	-
Week 16*:	Final
Textbooks and materials:	Güncel derleme ve araştırma makaleleri
Recommended readings:	Güncel derleme ve araştırma makaleleri
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:		0
Other in-term studies:		0
Project:		0
Homework:	1,2,3,4,5,6,7,8,9,10,11,12,13,14	70
Quiz:		0
Final exam:	16	30
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	7	14
Practice, Recitation:	3	14
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	0	0
Mid-term:	0	0
Personal studies for final exam:	0	0
Final exam:	0	0
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)