The student is capable of analyzing protein 3D- structures using a molecular visualization program and by (web-based) computational techniques. Additionally, the student will learn how to create and asses the quality of protein predictions.
The student is capable of formulating and understanding structure-function relationships of biomolecules and can apply such knowledge to the understanding of biological/medical problems.
Additionally, the student will have gained a deeper insight in the use of Machine Learning/Deep Learning techniques and their application in the 3D-structural field.
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This course has two parts: 1) structural biology, and 2) AI (artificial intelligence). The first part of the course introduces detailed aspects of biomolecular structures. Subsequently, structural aspects of important biomolecular processes, such as transcription, trans-membrane signal transduction, transport and mobility, will be discussed. During the second half the details will be placed in machine learning especially neural networks in the field of structural biology. The students will learn python, basic machine learning, and basic neural networks. The students will gain hands-on experience with Machine Learning techniques and the creation of Neural Networks.
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The course is aimed at 3rd year bachelor students of MLS and CHEM. Other students with a strong molecular background could theoretically follow the course as well but it might be wise to contact the coordinator. Also, check the presumed foreknowledge.
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The course will build upon general knowledge about atoms/molecules and proteins gained during the first 2,5 years. The course Data:Bioinformatics (MOL152) is a compulsory course in the MLS curriculum, but a higher grade during that course will help you in this one. Programming knowledge in either Matlab or Python will be necessary during the 2nd part of this course. Also, basic linear algebra and basic differential calculations are required for the 2nd part of this course.
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Midterm exams, written final exam and group-work. Active participation and participation are taken into account in the assessment.
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The course is scheduled 2 days per week, full time (yes, 9 to 5). Presence and active participation is required.
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