Duration
10h Th, 30h Mon. WS
Number of credits
| Master in bio-informatics and modelling, research focus | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course introduces machine learning algorithms (neural networks, decision trees, Bayes classifiers,...) as well as meta-heuristics, especially the biologically inspired ones (genetic algorithms, ant colony optimization algorithms, simulated annealing,...). Many methods discussed here will be used in R in order to extract a gene signature from RNA-seq data.
Learning outcomes of the learning unit
Getting familiar with machine learning and bioinspired meta-heuristics basic principles, up to being able to use them in R.
Prerequisite knowledge and skills
Basic R knowledge should have already been acquired (for example through STAT0077 or OCEA0224, the biological data analysis courses).
Planned learning activities and teaching methods
An important part of the course will be devoted to the implementation of very simple examples in order to get familiar with algorithms functionning.
Mode of delivery (face to face, distance learning, hybrid learning)
Presential classes, exercises on computers during the classes as well as as homeworks.
Course materials and recommended or required readings
Other site(s) used for course materials
- Site web du cours (http://www.bioinfo.uliege.be/classes/INFO0954/)
Further information:
http://www.bioinfo.uliege.be/classes/INFO0954/
Reference
Statistics and Data Analysis for Microarrays Using R and Bioconductor (Chapman & Hall/CRC Mathematical and Computational Biology) 2nd Edition
by Sorin Draghici
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
Oral presentation of a classical method or a scientific article, with additional theoretical question related to the course. An R project (as homework) will contribute to the evaluation.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Prof. Patrick Meyer,
BioSys Lab
e-mail: Patrick.Meyer@uliege.be
tel : 04366 3030
Association of one or more MOOCs
There is no MOOC associated with this course.