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Course module: BMB508219
Analytics and Algorithms for Omics Data
Course infoSchedule
Course codeBMB508219
ECTS Credits3
Category / LevelM (M (Master))
Course typeCourse
Language of instructionDutch
Offered byFaculty of Medicine; Graduate School of Life Sciences; Cancer, Stem Cells and Development Biology;
Contact personprof. J Ridder
Course contact
prof. J Ridder
Other courses by this lecturer
Teaching period
MASTER  (20/08/2018 to 17/08/2019)
Teaching period in which the course begins
Time slot-: Timeslot not applicable
Study mode
Remark3 - 21 June 2019. Please register via the CS&D website:
Course application processadministratie onderwijsinstituut
Enrolling through OSIRISNo
Enrolment open to students taking subsidiary coursesYes
Waiting listNo
Course placement processadministratie onderwijsinstituut
Course goals
Learning goals:
Please formulate specific learning goals
At the end of the course the student:
  • can read and understand a paper in current computational and systems biology literature,
  • identify relevant parts in the paper on the topic of data generation and the algorithms used to analyse these data and criticise the computational approaches taken,
  • list and describe several high-throughput data types and computer algorithms to analyse these data and motivate why a certain algorithm is suitable for the analysis of a certain data type,
  • apply the algorithms discussed in this course to toy problems, and derive and design adaptations of these algorithms for new data types,
  • draw biologically meaningful conclusions from results obtained with a analysis algorithm.
  • understands and can explain the basics of unsupervised Machine learning (ML) and the specifics of k-means, hierarchical and spectral clustering
  • understands and can explain the basics of supervised Machine learning (ML), including concepts such as cross-validation and overtraining and the specifics of probabilistic, knn and random forest classifiers
  • understands and can explain the basics of dimension reduction and the specifics of PCA, NMF and tSNE.
  • understands and can explain the basics of Hidden Markov Models and their application to (epi)genomic data
Period (from-till): 3 June 2019 - 21 June 2019

Name, faculty/department, participation (%) in course
Dr. Jeroen de Ridder, UMC University, 100%

Extended course description (for Osiris):
Bioinformatics is at the heart of many modern genomics research, 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 in finding the genetic origins of various diseases, such as cancer, diabetes or alzheimer. 
In this course we will study some key examples of bioinformatics analyses, i.e. data analytics and computational algorithms, by reading a set of selected papers that present some significant biological conclusions. Instead of the teachers giving lectures about the methodologies, the students are stimulated to read, study and comprehend the available course material. Some lectures will be provided to ensure the basic concepts are clear.
Schedule: The course runs for five days from 9.00 till approximately 17.00. Each day will include two rounds of paper discussions and two lectures that goes into depth with regards to the computational approaches taken. The second week of the course is for proposal writing and peer review of the proposals.
  • Unsupervised learning, Hierarchical and k-means clustering, spectral clustering
  • Supervised learning, cross-validation, overtraining, Bayes classifier, Random Forest classifier
  • Dimension reduction, PCA, NMF, tSNE
  • Hidden Markov Models, Forward Backward algorithm, Viterbi
Literature/study material used:
Provided course materials (slides) will be made available through our online learning platform:
Please register online on the CS&D website:
Bioinformatics Profile students will have priority when this course is followed as a part of their profile.
Thereafter, registration is on 'first-come-first-serve' basis until the maximum number of 20 participants is reached.
Mandatory for students in own Master’s programme:
Optional for students in other GSLS Master’s programme:
Prerequisite knowledge:
Basic knowledge of Linear Algebra and Statistics.
Entry requirements
Required materials
Instructional formats



Final result
Test weight100
Minimum grade5.5

Active participation 30%
Research proposal 70%

Kies de Nederlandse taal