Modeling Life
Have you ever wondered how cells “know” which genes to express at a given time and place, or how a plant seed “decides” when is the best time to germinate? Or would you like to know what the stripes of a zebra, the fingers on your hand, and the patterning of vegetation in dry ecosystems
Data Driven Discovery in the Life Sciences: Hypothesis Generation from Omics Data
Across the life sciences, scientists utilize omics data to study biological phenomena in humans, plants, animals and microbes. This results in large and heterogeneous data sets that can be analyzed using a variety of algorithms and statistical methods. Making sense of the data, extracting biological knowledge out of the results of these analyses and formulating
Biological Discovery through Computation
Multiple types of -omics data are rapidly changing the face of biological research. In the Bioinformatics minor, students have been exposed to fundamental computational techniques to analyse and visualise omics data. Students enrolled in the Omics minor have also explored technologies and data analysis methods to deal with the “wet” and “dry” components of omics
Computational Biology
This course focuses on using computational modelling to explore biological systems and test specific hypotheses. Students learn to construct exact models and analyse their behaviour to gain insight into the original biological system. The course draws on a broad range of biological questions across evolutionary, developmental, ecological, and molecular biology. Topics include evolutionary dynamics such
Data Analysis for Biosystems Engineering
The following topics will be addressed in the course: linear regression and multiple linear regression, including model formulation, meaning of model parameters, checking model assumptions and prediction; data transformation; experimental design, including completely randomized design, block design and factorial design, and calculating the required sample size to obtain a certain precision; analysis of variance and
Advanced Biotechnology
Biotechnology is a broad term for all technologies that utilize biological systems, living organisms, or their parts to develop or create different products. This course covers topics ranging from the development of climate-resilient and herbicide-resistant crops using CRISPR/Cas to the development of mRNA therapies and the usage of fungi for removing contaminants from the environment.
Research in Bioinformatics
Bioinformatics is at the heart of many modern systems biology analyses, and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. In essence, bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role
Data Mining
The goal of the course is to teach students how to think like a data miner. Intuitively, this means you have the mindset and skills to find practical solutions to common problems you encounter when extracting knowledge, patterns, and models from large data sets. To make such solutions effective, you must understand both the underlying
Keystone Project III: Big Data
Welke genen worden geactiveerd bij stress? Waarom bevorderen sommige bacterieën immuniteit? Hoe ontstaat kanker? Hoe wordt een microbe een ziekteverwekker? Om dit soort vragen te beantwoorden worden er vaak grote datasets –“Big Data” gecreëerd. In deze gevallen is analyse van de resultaten met eenvoudige middelen (Excel etc.) niet meer werkbaar. Om hiermee om te gaan
Machine Learning (WUR)
Machine Learning deals with algorithms that predict certain outputs (such as crop yields or traits) given previously unseen input data from cameras, other sensors, maps, molecular measurements etc. These algorithms learn how to do so using training data (sets of input examples, usually with corresponding outputs). Machine learning plays an increasingly important role in many