Leiden University

CS Bioinformatics Track 2016/2017

(Last updated: 6 - 3  2017)

Contents

 

 



News: CS Bioinformatics Kick-Off 2016 (slides)

The CS Bioinformatics Kick-Off will take place on Monday September 5th 2016 from 14.00 - 17.00 in Room B01 of the Snellius Building, Niels Bohrweg 1 in Leiden. Please find the program here.

 

Description

The main focus of the Bioinformatics specialisation is on Data Analysis and Modeling, which represents the unique expertise of the different research groups of Leiden University and the Delft University of Technology participating in this research oriented specialisation. This expertise is used to address issues like data capturing, data warehousing, data analysis and data mining that have become major challenges in the field of Bioinformatics due to the tremendous complexity and abundance of quantitative data in biology and medicine. On the other hand, bioinformatics heavily contributes to the identification of new fundamental computer science principles and the development of new informatics tools.

Bioinformatics offers a unique new synthetic approach for formulating hypotheses and solving problems in (molecular-) biology versus the classical reductionistic approach.

 

Qualifications for admission


Students from any university in The Netherlands with a BSc degree in Computer Science or with a BSc major in Computer Science will be admitted to the programme.

For all other (national and international) candidates, the Board of Admissions will judge the equivalence of their previous training to these BSc degrees.

Programme

The programme is 120 EC in extent. The programme is outlined below.

Core Programme

Level

EC

Pattern Recognition

500

6

Databases and Data mining

500

6

Functional Genomics and Systems Biology

500

6

Computational Molecular Biology

500

6


Every student of the Bioinformatics specialisation has to complete the core programme.


Specialisation Courses  (24 EC)

A choice can be made out of different specialisation courses. The specialisation courses have level 500, and range from 3 - 6 EC. The total of 24 EC is indicative and depends on the extent of the studentís support programme and research assignment. The selection of the specialisation courses takes place in coordination with the Bioinformatics specialisation study adviser.

 

Support Programme   (max 12 EC)

For each student a support programme will be defined by the Bioinformatics specialisation study adviser. The support programme consists of tutors or courses in Life Science, Computer Science, Mathematics, or of optional courses for deficiency programmes. The support programme will consist of a maximum of 10 EC.

 

Research assignment  (15 EC)

Masterís research project (45 EC)

Schedules

Course Descriptions

Detailed Programme

Core Program (24 EC)

  • Databases and Data Mining
  • Pattern Recognition (TUD, IN4085) (schedule sept'16)
  • Computational Molecular Biology
  • Functional Genomics and Systems Biology (TUD, IN4176)

Specialization Courses (24 EC)

  • Swarm-Based Computation with Applications in Bioinformatics
  • Evolutionary Algorithms
  • Complex Networks
  • Multicriteria Optimization and Decision Analysis
  • Social Network Analysis for Computer Scientist
  • Mathematical Biology Metabolic Network Analysis
  • Neural Networks
  • Image Analysis in Microscopy
  • Multimedia Information Retrieval
  • Bayesian Networks
  • Advanced Bioinformatics (TUD, IN4329)
  • Advanced Image Processing (TUD, ET4283)
  • Data Visualization (TUD, IN4086)
  • Methodology of Science and Engineering (TUD, WM0332IN)

Support Program (Max 12 EC)

Research Project (15 EC)

Master Thesis Project (45 EC)

Previous Master Theses

2014 - 2015

Hoogenboom, Jerry: Characterisation and Filtering of Systemic Noise in NGS Data with Applications in Forensics

Liem, Michael: Characterizing mapk signaling in different cancers Through large public datasets

Neuteboom, Jonathan: Protein structure prediction by Iterative fragmen T Asssembly (PITA) (CS)

Xia, Zhihan.: A Metadata Validation Process Design for an Automated High-Throughput Screening Workflow - Case Study in Metadata of CytomicsDB (CS)

2013 - 2014

Jonathan den Boer: Development of Quality Assessment Methods for De Novo Transcriptome Assemblies & Expression Analysis

Jasper Linthorst: Probubble: variant calling through the alignment of overlap based de-novo assembly graphs

Yuxiang Liu: Gene Name Normalization by using Deep Belief Networks (CS)

Joeri Meijsen (Bio-informatics): Identifying soft selective sweeps in the human genome from the spatial distribution of strong selective sweeps

Dimitrios Palachanis:Using the Multiple Instance Learning framework to address differential regulation

Johanna Pagano (Bio-informatics): Multi-scale Analysis of Enhancer Gene Relations in Nine Inbred Rat Strains

Dimitra Zafeiropoulou (bio-informatics): Genotypes-phenotype predictions in patients diagnosed with early onset Alzheimer

2013

Youri Hoogstrate: A Voyage Through Protein Sequence Space

Dimitar Kolev: Prediction of ProteinThree-Dimensional Structure

2012

Thies Gehrman: Conditional random fields for protein function prediction

2011

Alexey Gritsenko: Scaffolding of Next-Generation Sequencing Assemblies Using Diverse Information Sources

Orr Shomroni: A Gaussian Random Field Algorithm used to Analyse MicroArray Data from Multiple Species

2010

Saskia Hiltemann: Dynamical Systems: Connected Predator-Prey Systems

Ruifang Li: Immune Response to Prostate Cancer - Exploration of Normalization, Feature Selection and Classification Procedures

Alexander Aleman: Self-adaptive Mutation in Molecular Evolution

2009

Zi Di: Systematic Evaluation of Image Analysis for Cell-Matrix Adhesion Studies in Cytomics

2008

Kuan Yan: Object Tracking and Data Analysis of Migrating Tumor Cells Visualized by time-lapse video

Yan Zhang: Estimation of 3D Motion in time sequences of volumetric MRI data (CS)

Anyi Zhang: Bayesian Mixed Integer Optimization Using A-Priori Knowledge on Variable Dependences

2006

Remco van Kerhoff: Analysis of DNA Sequence Using Signal Processing Techniques (CS)

Maarten van Iterson: Collecting and Analyzing Natural Variants in G Protein Coupled Receptors


Registration at TU Delft (updated)

You need to register as a (bijvak)-student at TU Delft as you will need access to computers, blackboard, courses and exams. The registration procedure that you have to follow in order to obtain the TU Delft blackboard account is as follows:

  1. Register through 'studielink' as a (guest-)student at TUDelft. (You may have to click through some additional questions.)
  2. Obtain a proof of payment for your study at the student-administration at Leiden University
  3. Send or deliver the original proof of payments to the student administration in the hal of the EWI building, and send a copy to Prof. dr. ir M.J.T. Reinders at TUDelft
  4. Prof. dr. ir M.J.T. Reinders will be contacted by the student administration at TUDelft and inform that you are a Bioinformatics student following our track.
  5. After this you will obtain a TUDelft blackboard account that you need to activate at the TUDelft student administration

Note: this of course assumes that you completed your registration at
Leiden University first.

If you have any further questions or encounter any problems please contact me.

Registration at Leiden University

If you want to follow courses like Databases and Data Mining at Leiden University, you have to register as a guest-student. To register as a guest-student please follow the procedure described at the following page:

http://www.students.leiden.edu/application-admission/other-modes-of-study/other-modes-of-study.html


Contact

For all questions about the CS Bioinformatics track program, admission, study plan, graduation, etc. Please contact:

Dr. E.M. Bakker
Room 145
Snellius Building
Niels Bohrweg 1
Leiden
E-mail: E.M.Bakker@liacs.leidenuniv.nl