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|Notices (updated 16.10.2007)
|Lecturer||D.Sc.(Tech.) Janne Nikkilä, Laboratory of Computer and Information Science|
|Assistant||M.Sc.(Tech.) Leo Lahti, Laboratory of Computer and Information Science|
|Semester||Autumn 2007, period I and II|
|Seminar sessions|| Thursdays 12.30-14.00 in lecture hall T5 in computer science
Konemiehentie 2, Otaniemi, Espoo. The introductory session on September 20th.
|Registration||TKK students: WebTopi, others: send mail to email@example.com|
In computational modeling tasks some background data or prior knowledge is usually available in addition to the actual data concerning the primary task. This background information could be useful in analysing new experiments. 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 internet become more and more populated by knowledge and data 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.Prerequisites [back to top]
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.
Journal club (abstract + presentation + project work) (5 cr).
The course is graded as fail/pass/pass with distinction. The abstracts+presentations and the project works are graded as fail/pass/pass with distinction. To pass the course, both presentation+abstract and project work must have at least pass grade.
Make a presentation about a subject chosen during the first sessions. Prepare a one page extended abstract about your subject and send it electronically to the assistant at least 48 hours before your presentation. The assistant will then comment the abstract if needed, and you will send the corrected abstract to the assistant at least 24 hours before your presentation, and the assistant will send it to the other participants of the course.
Complete the project work and return a concise written report about it.
Attend the seminar session and the discussion actively.
|TKK students:||In Webtopi|
|Other universities:||Send email to the organizers or sign up at the introduction lecture.|
Introductory material (read before the course):
|Time||Lecturer||Subject and material|
|20.9.||Janne Nikkilä, Leo Lahti||
|4.10.||Andrey Ermolov||Integrative missing value estimation for microarray data. Hu et al., BMC Bioinformatics, 2006. (html)|
|11.10.||Lauri Lyly||Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Carvalho et al., Biostatistics, 2007. (html)|
|18.10||Jaakko Peltonen||Multitask learning|
|25.10||Andrey Ermolov||Penalized Probabilistic Clustering. Z. Lu and T.K. Leen, Neural Computation, 2007. (html)|
|1.11||Lauri Lyly||Logistic regression with an auxiliary data source. Liao et al., ICML 2005. (pdf)|
Write a short (A4) project plan before the deadline and send it to the course assistant. Our suggestions and approval will be made as agreed in the course.
DL for the project will be decided later. For more details, see the separate page.
For more information, please send email to firstname.lastname@example.org.
Janne Nikkilä and Leo Lahti
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