Big Data & Applications in biomedicine

Faculteit Science and Engineering
Jaar 2021/22
Vakcode WMBM025-05
Vaknaam Big Data & Applications in biomedicine
Niveau(s) master
Voertaal Engels
Periode semester II a
ECTS 5
Rooster rooster.rug.nl

Uitgebreide vaknaam Big Data & Applications in biomedicine
Leerdoelen At the end of the course, the student is able to:

1) apply R/Python and statistical tools to analyze datasets from different biomedical field such as in imaging;

2) understand the importance of data analysis tools in analyzing biomedical data;

3) understand and apply the principles of pre-processing, deep learning, model selection, statistical validation and assessment of a model;

4) familiarize with application examples of data sciences and machine learning in the medical field;

5) apply pre-processing, statistical and modeling tools to a real dataset and visualize and present the analysis to the other students
Omschrijving An increasing volume of data is becoming available in biomedicine and healthcare, from genomic, proteomics, to electronic patient records, (radiology) images and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare.

In this course, you will learn about some of the different types of data and computational methods involved in healthcare. You will have hands-on experience of working with such data from single cell-sequencing, multi-modular analysis from coupling neuro-imaging-behavior and genetics, and methylation- immunochemistry-proteomics from oncology. This course will show how the theory of statistical inferences and programming can be applied to advance medical research which ultimately benefit patient care.

Concepts for effective analysis of images such as thresholding, morphological operations (dilation, erosion, closing, opening), histogram equalization, will be explained before introducing convolutional neural networks with deep learning.

And you will learn from leaders in the field about successful case studies. Topics include: (i) Data Processing, (ii) Image Analysis, (iii) Network Learning and Modelling, (iv) Machine Learning.
Uren per week
Vaksoort master
Entreevoorwaarden Basics of programming and datamanagement
Applied statistics and modelling
Opmerkingen
Opgenomen in
Opleiding Jaar Periode Type
MSc Biology: Modelling in the Life Sciences  (Electives/Optional modules) - semester II a keuze
MSc Biology: Research  (Electives/Optional modules) - semester II a keuze
MSc Biology: Science, Business and Policy  (Electives/Optional modules) - semester II a keuze
MSc Biomedical Sciences: Biology of Ageing  (Electives (10 ECTS)) - semester II a keuze
MSc Biomedical Sciences: Biology of Cancer and Immune System  (Electives ) - semester II a keuze
MSc Biomedical Sciences: Biology of Food and Nutrition  (Electives (10 ECTS)) - semester II a keuze
MSc Biomedical Sciences: Neuroscience  (Electives (10 ECTS)) - semester II a keuze
MSc Biomedical Sciences: Research  (Electives) - semester II a keuze
MSc Biomolecular Sciences  (Electives/Optional modules) - semester II a keuze
MSc Medical Pharmaceutical Sciences: Drug Toxicology & Translational Technology  (Electives) - semester II a keuze
MSc Medical Pharmaceutical Sciences: Pharmaceutical Design and Engineering  (Other possible Electives) - semester II a keuze
MSc Medical Pharmaceutical Sciences: Pharmacoepidemiology & Pharmacoeconomics  (Electives) - semester II a keuze
MSc Medical Pharmaceutical Sciences: Research  (Electives) - semester II a keuze
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