Akkerbouw, precisielandbouw, duurzaam bodembeheer

Life-long learning: Heavily geared towards people who work in the arable/horticulture sector themselves or provide advice. Also extensive expertise in bio/nature-inclusive. Central and Northern NL

Details

Institution: Aeres Training Centre

EC: LLO ec

Algorithms for Network-based Bioinformatics

We will cover topics such as complex network models to characterise and analyse biological systems, approaches to infer network structures from given biological measurements, strategies of network enhancement through network integration, predictions based on network structures, and graph generation (molecular design).

Specifically, the course contains the following topics:
Background on molecular data: systems biology, data-driven approach, high throughput measurements, sequencing, mass-spectrometry, immunoprecipitation, single cell measurements, molecular properties, molecular descriptors, databases;
Molecular interactions: transcription factors, protein complexes, metabolic reactions, gene-protein-metabolite interactions, interaction models;
Frameworks to work with complex systems: graphs with nodes/edges, directionality, layers, heterogeneous graphs, topological graph metrics, graph characteristics (erdos-renyi/scale-free/hierarchical networks), hypergraphs, topological hypergraph metrics, simplicial complexes, simplicial complex metrics;
Network inference and simulation: rate-law modelling of RNA regulation model, dynamic system/steady state, reduce, association networks, linear networks, design matrix, bayesian networks, boolean networks, regularisation, evaluation;
Network enhancement: heterogenous networks, reliability of networks, bias/variance, evaluation strategies, early/intermediate/late integration, machine learning approach, decision trees, classifier combining, probabilistic/dissimilarity/kernel-based representations, network fusion, random walks;
Network-based predictions on the node-level: guilt-by-association, maximise coherency, Markov random field, clustering based on graph properties, Markov clustering, spectral clustering, Louvain method, active modules, validation;
Network-based predictions on the graph level: graph neural networks, graph convolution, node message passing, graph attention, molecular property prediction, structure-based function prediction;
Graph generation for molecular design: generative AI for molecular design, graph variational autoencoders, graph diffusion models

Program/Minor

Learning Outcomes

Give examples of the basic elements of a living cell, their interactions, and ways how the interactions can be measured and why computational models are used to model the interactions; Explain the types and functional principles of different mathematical models used to represent and model network problems; Examine how network models can be used to analysis, enhance, mine, and infer information to gain further insights into a specified biological problem; Argue upon recent network-based computational biology literature paying attention to proposed methods, advantages, and disadvantages, potential modifications of the method and alternative biological applications; Formulate your own written network-based research proposal with a biology-oriented research problem, a motivated and innovative algorithmic solution, and well-reasoned evaluation strategies; in context of the materials presented throughout the course;

Details

Institution: TU Delft

Period: Period 3

Level: Master

EC: 5 ec

Algorithms for sequence-based Bioinformatics

After having followed this course, the student has a good understanding of algorithms and data structures in genomics used for DNA sequence analysis. The student is able to implement algorithms in python, and can translate methods described in scientific literature into a working implementation.

Bioinformatics analyses in genomics aim to compare large sets of genomes in order to understand and explain differences in traits of an organism. Contemporary methods are powered by fundamental algorithms and data structures, which are efficient and scale to large data sets. A thorough understanding of these algorithms and data structures is necessary for advanced users and developers in this area.

Bioinformatics application domains that are covered include: variant calling, motif detection, genome assembly, sequence alignment, DNA sequencing.

Program/Minor

Learning Outcomes

Explain multiple sequencing technologies and their data properties; Analyse core data structures and foundational computer science algorithms in sequencing-based applications of bioinformatics; Implement conceptual data structures and algorithms with applications in genome sequence analysis; Implement published algorithms in a real-life setting; Benchmark a published sequence-based algorithm; Produce scientifically sound written and oral communication of a benchmark study that compares the student's implementation against a literature implementation.

Details

Institution: TU Delft

Period: Period 2

Level: Master

EC: 5 ec

Algorithms in Bioinformatics

Modern biology routinely generates huge amounts of data: sequences, from NGS experiments; quantitative data, from -omics experiments; and graphs, representing molecular interactions. At the heart of many bioinformatics applications are algorithms that handle such types of data in time- and memory-efficient ways. Almost invariably these algorithms optimize some criterion – e.g. alignment quality, energy function or probability measure – using the data available.
In this course, the main types of algorithms will be discussed and implemented, aiming to gain a deeper understanding of the computational strategies underlying these algorithms. This will allow students to recognize which type of algorithm will be applicable in the development of new bioinformatics tools to help answer new questions in (computational) biology.

Program/Minor

Learning Outcomes

Identify the algorithmic design technique and the underlying optimization criterion in bioinformatics algorithms; Explain in detail the working of a number of fundamental bioinformatics algorithms; Implement a given bioinformatics algorithm in Python; Analyse algorithms discussed in bioinformatics/computational biology literature

Prerequisites

Advanced Bioinformatics

Details

Institution: WUR

Period: Period 6

Level: Master

EC: 6 ec

Applied Machine Learning

Machine learning is marking a revolution in the world. From an academic research topic, over the last decade it has shift to a major paradigm used in many companies for a wide range of services. From deleting SPAM mail from your inbox to ranking the Google search results, and from defining your Facebook stream to enabling medical diagnoses.

In this course we study learning from large collections of unstructured data, such as text documents, web pages, images and videos. We address the complete machine learning chain, from designing the system and its objectives, to representing data and selecting and evaluating the learning method. We review and focus on the foundations of supervised classification and regression, which we extend to deep learning. We have application lectures on text processing, computer vision and recommender systems.

You will learn the theoretical concepts during the lectures with a keen eye on the design of the full learning system. In the tutorials we will focus on some off the important mathematical concepts, and in the lab you will gain hands-on experience through a number of coding assignments (implementing your own deep learning functions) and by participating in a Kaggle competition or project. Finally, a few experts from the field (both academic as well as industry colleagues) are invited to provide guest lectures.

Program/Minor

Learning Outcomes

Discuss the high impact and potential of machine learning; Create a machine learning system that is optimized for a given task; Analyze visual and textual data representations; Explain their machine learning results and findings in a poster presentation; Explain what relates and differentiates classification and regression; Describe the different layers of a deep network; Program key machine learning system

Prerequisites

Basic math and programming skills are mandatory.

Details

Institution: UvA

Period: Period 2

Level: Master

EC: 6 ec

Applied Plant Biology 

This course consists of 4 modules. Information/insights obtained within previous modules will be implemented in later ones.
Module 1: Masterclasses by guest-lecturers from the green sector; academia, industry, finance and (semi)governmental organizations.
Teaching/learning format: Lectures + discussion, writing assignment

Module 2: Identifying current challenges in applied molecular Plant biology.
Teaching/learning format: Project presentation + discussion

Module 3: Developing a research project and writing a proposal on applied aspects of the subject chosen in module 2.
Teaching/learning format: Developing and writing of a project proposal

Module 4: Reviewing of research proposals as member of a granting committee
Teaching/learning format: Written review, rebuttal, short presentation + discussion

Program/Minor

Learning Outcomes

Has obtained a broad overview and understanding of “the green sector” in the Netherlands and abroad, with the focus on the plant breeding industry and biotechnology, the role of fundamental research therein, and job opportunities for young MSc’s with a plant molecular biology/biotechnology background; Knows how to apply conceptual tools (knowledge, techniques) and competences (techniques, writing skills, project management, etc.) to real-life practical contexts that exemplify the type of work he/she may do in a future ‘green’ career; Has obtained insight in the interaction between green sector companies, organizations, societal stakeholders and academia and their specific interest;

Prerequisites

Molecular plant physiology, as for example provided through the study track Plant Biology in the Biology BSc at UU.

Details

Institution: UU

Period: Period 1

Level: Master

EC: 4.5 ec

Artificial Intelligence in (Bio-)Chemical Engineering

The digital transition of the (bio)-chemical industry and research requires new intelligent knowledge and decision-making tools. The increasing availability of data and computational resources over the past decade has led to a resurgence of machine learning-based research. Artificial intelligence has significant advantages over traditional modeling techniques, including flexibility, accuracy, and speed of execution. Therefore, artificial intelligence holds great potential in the digital transformation and full automation in the (bio)-chemical industry.

This course aims to introduce the concepts of artificial intelligence and machine learning and discuss the potential and limitations of different machine learning methods for given (bio)-chemical engineering applications. Moreover, a machine learning and deep learning framework will be introduced during the course, including hands-on practice.

Program/Minor

Learning Outcomes

Explain the concepts of artificial intelligence and data analysis, and the potential and limitations thereof on (bio-)chemical engineering applications (Cognitive level: understand); Develop an artificial intelligence system to solve a given (bio-)chemical engineering problem using existing artificial intelligence methods. (Cognitive level: create); Implement a given or developed artificial intelligence system using Python. (Cognitive level: Apply); Evaluate the results obtained with an artificial intelligence system in the context of (bio-) chemical engineering applications. (Cognitive level: Evaluate)

Prerequisites

MSc level numerical methods and Python programming

Details

Institution: TU Delft

Period: Period 3

Level: Advanced

EC: 3 ec

Basic Machine Learning for Bioinformatics

Modern biology increasingly relies on vast datasets generated through genomic, transcriptomic, and single-cell omics techniques. To make sense of these large-scale data and extract meaningful patterns or predictions, machine learning (ML) has become essential. This course introduces students to the foundational principles and techniques of ML as applied to biological data. Students learn the differences between supervised and unsupervised learning and implement key ML algorithms from scratch using Python, including linear and logistic regression, neural networks, k-nearest neighbors, and hierarchical clustering. They also use dimensionality reduction methods such as PCA. The course emphasizes core concepts like cost functions, gradient descent, regularization, the bias-variance trade-off, and performance evaluation using metrics such as ROC-AUC, accuracy, and recall. After building a conceptual foundation in week one, students apply their knowledge using scikit-learn and work in groups on a biological classification project using real-world data.

Program/Minor

Learning Outcomes

Understand and explain the difference between unsupervised and supervised learning; Implement cost functions and gradient descent for supervised learning; Build and vectorise implementations of linear/logistic regression and neural networks; Recognize and address overtraining and bias-variance tradeoffs using cross-validation and regularisation; Select and tune hyperparameters for learning algorithms; Implement k-nearest neighbour, hierarchical clustering, and PCA; Evaluate classifiers using relevant metrics (ROC-AUC, precision, recall, etc.); Use and understand the scikit-learn ML framework in Python; Apply ML methods to real biological datasets in a team project; Visualise ML performance using plots like ROC and learning curves.

Prerequisites

Students are expected to have basic familiarity with Python. This includes the ability to write simple functions, use for-loops, and manipulate lists to perform calculations.

Details

Institution: UU

Period: Period 1

Level: Master

EC: 3 ec

Big Data Processing

The term “Big Data” describes datasets that are either too big or change too fast or both to be processed on a single computer.

Big Data Processing provides an introduction to systems and algorithms used to process Big Data. The main focus of the course is programming and engineering big data systems; initially, the course explores general programming primitives that span across big data systems and touches upon distributed systems, databases and filesystems. Then, the course examines in detail the implementation of data analysis algorithms in Spark and Flink, in the context of batch, streaming, and graph processing applications.

Program/Minor

Learning Outcomes

Apply basic data processing operations (filtering, folding, projecting, etc); Explain basic techniques (vector clocks, consensus) in distributed systems; Explain basic data management techniques in distributed databases; Explain the major components of batch processing systems; Create batch algorithms for novel (unseen) practical problems; Explain the difference between iterative/non-iterative algorithms; Explain basic graph algorithms and their implementation batch processing systems; Describe in which scenaria streaming algorithms are most applicable; Apply basic streaming algorithms to practical problems

Details

Institution: TU Delft

Period: Period 1

Level: Intermediate

EC: 5 ec

Bioinformatics

De ontwikkeling van steeds nieuwe en betere high-throughput assay technologieën transformeert de moleculaire biologie in een razendsnel tempo. Sequencing van volledige genomen en de mogelijkheid om RNA, eiwitten, metabolieten en hun interacties op grote schaal (genoom-breed) te meten brengen een revolutie teweeg in biologisch onderzoek en dientengevolge ook op de diagnostiek. Voor het eerst komen verschillende soorten data kwantitatief beschikbaar, en in enorme hoeveelheden. Deze genoom-brede datasets krijgen een steeds grotere impact op de diagnostiek zoals prenataal testen of voorspelling van de progressie van een ziekte.

Als gevolg van deze ontwikkelingen zal een toekomstig klinisch technoloog zich bewust moeten zijn van deze genomische data, bekwaam zijn in: het begrijpen van de onderliggende data, hoe deze data geanalyseerd wordt, en hoe deze data geïnterpreteerd dient te worden in het kader van diagnostiek en onderzoek. Klinisch technologen zullen hiervoor een stevige basis moeten hebben in relevante statistische en data analyse methoden om hun werk goed te kunnen doen.

Dit vak richt zich op een aantal basis technieken om genomische data te analyseren, van elementaire statistiek (vinden van relevante verschillen) en clusteren (ordenen van de data) tot classificeren (voorspellingen doen aan de hand van de data). Ook wordt er aandacht besteed aan de verschillende high- throughput meettechnieken en de noodzakelijke voorbewerking van de data.

Focus is on humans and health

Program/Minor

Learning Outcomes

Kan een aantal representatieve analysetechnieken uit de statistiek (toetsen, clustering en classificatie), benoemen, kwalitatief uitleggen, en toepassen op genoom-brede datasets; Kan de student, gegeven een biologische vraagstelling, de meest toepasselijke analysetechniek kiezen, zelfstandig toepassen en de resultaten interpreteren; Kan de student uitleggen wat de invloed van de instelling van de verschillende parameters op de resultaten zijn

Details

Institution: TU Delft

Period: Period 2

Level: Intermediate

EC: 3.5 ec

Bioinformatics and Dynamic Modelling

This course introduces students to the research fields of bioinformatics and biological modeling. Central themes are the use of data to extract underlying patterns on function and evolution, and the use of models to test hypotheses and make predictions for biological systems.

Introduction:
Biological processes are notoriously complex, and studying their dynamics through modeling and simulation is an effective way to understanding their key properties. In the first part, the students will learn to model the dynamics of biological processes such as cell signaling, gene regulation, interaction between bacteria and phages. Students will be trained to read and interpret ordinary differential equations models, as well as write such models themselves. They will analyse the models using phase spaces and stability analysis of equilibria to predict the behaviour of these dynamical systems and make use of simulation techniques. Furthermore, they learn to give a biological interpretation of the results of such analyses to answer biological questions and critically assess the model on its validity.

Large-scale data collection (omics) is ubiquitous and highly informative in the field of life sciences. To appropriately use and interpret omics data, the new wave of life scientists needs to understand bioinformatics methods. In the second part of this course, students learn about fundamental bioinformatics algorithms to study biological sequences. They will use the results to predict protein function or reconstruct the evolutionary history of genes, proteins and species. Furthermore, they will interpret high-dimensional omics-data through visualization, hierarchical clustering and machine-learning.

Set-up of this course:
The student prepares each lecture by reading the indicated parts of the syllabus. Each lecture is followed by a tutorial session to get a deeper understanding of the content and to practice the required skills. Most weeks, in an additional tutorial, students work individually or in small groups on a graded assignment (six in total). Some tutorials are on pen and paper, others require the students to bring their own laptop. Both parts of the course are concluded by a written exam.

Program/Minor

Learning Outcomes

Analyze, interpret, and form an opinion on given Ordinary Differential Equation (ODE) models; Translate simple biological / chemical systems into mathematical models (ODEs); Discuss the suitability of different model formalisms according to the scientific question asked; Access, evaluate and employ various biological databases and bioinformatic tools on the internet; Discuss how different levels of conservation can be determined and used in various sequence alignment algorithms and interpret their results; Reconstruct and interpret phylogenetic gene or species trees based on (alignment) data; Interpret large omics data sets using data visualization, hierarchical data clustering and machine learning

Prerequisites

Entry requirements: Successfully completed MBLS-101, MBLS-102 and having followed MBLS-107

Details

Institution: UU

Period: Period 3

Level: Intermediate

EC: 7.5 ec

Bioinformatics and Evolutionary Genomics

The sequencing revolution is rapidly charting ever more obscure branches in the tree of life. All these novel genomes and increasingly sophisticated bioinformatic methods are changing our view of where the complexity in our protein complexes come from and how the genes in the human genome came from. For example when we trace compare the subunits of many protein complex, it seems they are much older than originally anticipated and that genomic differences between humans and baker’s yeast are the result of massive gene loss in fungi in addition to innovations in the animal and vertebrate genomes.

In this course we will learn how novel data, novel methods and novel concepts are driving this novel view of genome evolution. You will learn how to infer the evolutionary history of genes and proteins interest, utilizing basic (online) bioinformatic tools and your newly acquired knowledge of the interplay between genome evolution and function. The course consists of lectures, (computer) exercises and literature discussions. The first three days are focused on methods and concepts to describe genome evolutionary events. The remaining days are focused on important evolution and function events (such as the origin of eukaryotes or the whole genome duplication at the base of vertebrates) that have left their trace in e.g. the human genome.

Program/Minor

Learning Outcomes

Reconcile gene trees in terms of duplications, losses, horizontal gene transfer, endosymbiosis and lack of phylogenetic signal; Find homologs and assign orthologs and paralogous relations to genes; Understand how large scale orthology methods work and also how evolutionary processes such as gene loss, highly divergent sequences and gene fusions lead to problems for these methods; Give examples how deep homology detection has changed our view on genome evolution; Summarize the key elements in the transition of prokaryotes to eukaryotes; Understand how genomic information is leveraged to detect ancient whole genome duplication events; Describe how function of proteins evolves after gene duplication yet is generally conserved between orthologs; Understand how high-throughput data on gene function can in principle be used to study evolution of function; Be able to read and discuss bioinformatic research papers on the interplay between genome evolution and gene function;

Prerequisites

Assumed: Bachelor of science in the same field

Details

Institution: UU

Period: Period 2

Level: Master

EC: 3 ec