Basic Information
The Course Will Introduce Algorithmic, Statistical, and Machine Learning Techniques For Understanding The Human Genome, Epigenome, Evolution, and Disease Mechanisms. Every Lecture We Will Present A Topic From The Field of Bioinformatics, Including Its Biology Background, and Discuss Computational Concepts For Problem Solving. The Biological Applications We Will Deal With Include# Genetic Sequence Analysis, Gene Expression Analysis, Genomic and Epigenomic Regulation, Protein Folding, Genetic Variation and Associations, Disease Mapping, Population Genetics, Cancer Genomics and Single-cell Genomics. We Will Also Learn to Apply The Principles By Using Basic Tools and Major Databases in The Bioinformatics Field. The Course Includes 4 Hw Assignments and a Final Project to Be Presented in A Poster Day. Learning Outcomes# By The End of The Course The Students Will Be Able To# 1. Explain Fundamental Life Sciences Principles. 2. Explain and Demonstrate Key Algorithms and Sdtatistical Tools That Are Used to Solve Problems in Bioinformatics. 3. Analyze Different Types of Biological Data By Utilizing Computational Tools. 4. Design and Execute a (small-scale) Research Project Using Biological Databases and Computational Tools.
Faculty: Computer Science
|Undergraduate Studies
|Graduate Studies
Pre-required courses
(134058 - Biology 1 and 234112 - Programming )c() or (134058 - Biology 1 and 234114 - Introduction to Computer Science) or (134058 - Biology 1 and 234128 - Introduction to Computing With Python) or (134058 - Biology 1 and 234127 - Introduction to Computing With Matlab) or (134127 - Topics in Biology and 234127 - Introduction to Computing With Matlab) or (134127 - Topics in Biology and 234112 - Programming )c() or (134127 - Topics in Biology and 234114 - Introduction to Computer Science) or (134127 - Topics in Biology and 234128 - Introduction to Computing With Python)
Course with no extra credit
134158 - Tools in Bioinformatics For Life Science 234523 - מבוא לביואינפורמטיקה 234525 - Introduction to Bioinformatics M
Related Books
Semestrial Information
Weekly Hours
2.5 Academic Credit • 2 Lecture Hours • 1 Discussion Hours
Go to Course Page
Responsible(s)
Dvir Aran
Registration Groups
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Weekly Hours
2.5 Academic Credit • 2 Lecture Hours • 1 Discussion Hours
Go to Course Page
Responsible(s)
Dvir Aran
Registration Groups
|
|
|
|
|
|
Weekly Hours
2.5 Academic Credit • 2 Lecture Hours • 1 Discussion Hours
Go to Course Page
Responsible(s)
Dvir Aran
Registration Groups
|
|
|
|