Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

T-61.6080 Special course in bioinformatics II:
Prior knowledge and background data in computational inference V P, 5 cr

Lecturers D.Sc.(Tech.) Janne Nikkilä, Laboratory of Computer and Information Science,
Assistant M.Sc.(Tech.) Leo Lahti, Laboratory of Computer and Information Science
Credits (ECTS) 5
Semester Autumn 2007 (during periods I and II)
Seminar sessions On Thursdays at 12-14 in lecture hall T5 in computer science building,
Konemiehentie 2, Otaniemi, Espoo. The first session on 20.9.2007.
Language English
Format Seminar (abstract + presentation + project work) (5 cr).
Registration TKK students: WebTopi, others: send mail to The number of participants is limited to about 25. Major students at the arranging laboratories have precedence, and the others are accepted in the order of registration time.


In computational modeling tasks some background data or prior knowledge is usually available in addition to the data regarding the primary task. This background information could be useful in analysing the primary data. The issue is highlighted in bioinformatics where a single biological experiment always contains very few samples compared to the complexity of the system that generates the data (cell and organisms), and at the same time public databases in the internet are becoming more and more populated by data and knowledge from analogous experiments.

The main problem typically is that the background data is not directly related to the current task, or only part of it is known to be relevant: In bioinformatics the data is from slightly different experiments, organisms, measurement procedures etc. This setting requires advanced computational methods that are able to utilize expert knowledge and/or learn the relevance from the data.

This course is designed to introduce computational and statistical concepts and tools used at the moment in utilizing background data and prior knowledge, especially in bioinformatics. The course reviews techniques, for example, from relevant subtask learning, use of prior information by Bayesian methods, and applications of supervised learning.


This course is intended mainly for graduate students of computer science, statistics, and applied mathematics, but students from other fields are welcome as well. In particular mathematically oriented biology, bioinformatics, and medical students should benefit from the course.

Basic knowledge of probability, statistics, vector algebra, and calculus is assumed (the basic mathematics courses in HUT). A "Basic course in bioinformatics", such as S-114.2510 Computational Systems Biology or equivalent background is assumed as well.

Janne Nikkilä, Leo Lahti

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