Course Title: |
BIOINFORMATICS |
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Code: |
EPE_003 |
Semester: | Spring |
Weekly teaching hours | CREDITS (ECTS) | |
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Lecture: | 2 | 5 |
SYLLABUS |
The topic of the course is to introduce students to the basic notions of Bioinformatics. Bioinformatics is a combination of Computer Science and Molecular Biology focusing at the design of data structures and computational methods to facilitate the efficient storage, retrieval and manipulation of biological molecules (DNA, RNA, genes, proteins).
Understanding basic computational methods such as string and machine learning algorithms and how they are used to develop software tools to processefficiently biologicall data is a main objective of this course. Course introduction to its main notions (Computer Science and Molecular Biology). Exact string matching algorithms (Boyer – Moore, Knooth-Morris-Pratt, Aho-Corasick Automaton). Suffix Trees and applications in Bioinformatics. Inexact matching an sequence alignment. Multiple sequence alignments.Bioinformatics Databases and Data Mining. |
Learning outcomes |
Learning Outcomes for the course “Bioinformatics” are as follows:
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General Competences |
Computational Thinking: Apply computational and algorithmic approaches to analyze biological data and solve complex problems in Bioinformatics.
Data Analysis and Interpretation: Proficiency in analyzing and interpreting biological data using appropriate statistical and computational methods. Programming Skills: Develop programming skills to implement algorithms, manipulate biological data, and utilize Bioinformatics tools and software. Problem-Solving: Apply critical thinking and problem-solving skills to address challenges in analyzing biological data and designing computational solutions in Bioinformatics. Bioinformatics Tools and Databases: Familiarity with commonly used Bioinformatics tools, databases, and resources, and the ability to navigate and retrieve relevant biological information. Data Mining and Visualization: Utilize data mining techniques to extract meaningful patterns and insights from large biological datasets, and present findings through visual representations. Interdisciplinary Collaboration: Collaborate effectively with experts from both computational and biological disciplines to tackle complex problems in Bioinformatics. Ethical Considerations: Understand and adhere to ethical guidelines in handling biological data, ensuring privacy, and maintaining data integrity. |