[BioC] e-LICO multi-omics prediction challenge with background knowledge on Obstructive Nephropathy

Adam Woznica Adam.Woznica at unige.ch
Wed Sep 15 22:07:10 CEST 2010

Dear all,

We present a biological data-mining problem that poses a number of 
significant challenges; the available data: (i) are of high 
dimensionality but of extremely small sample size, (ii) come from 
different sources which correspond to different biological levels, (iii) 
exhibit a high degree of feature dependencies and interactions within 
and between the different sources; some of the interactions between the 
different sources are known and available as background knowledge, and 
(iv) are incomplete.

This data was obtained from patients with Obstructive Nephropathy (ON) 
which is the most frequent nephropathy observed among newborns and 
children, and the first cause of end stage renal disease usually treated 
by dialysis or transplantation. The goal is to construct diagnostic 
models that accurately connect the biological levels to the severity of 
the pathology. We particularly welcome data mining approaches and 
learning methods that are able to accommodate the available background 
information in order to address the formidable challenge of high 
dimensionality small sample size of our setting and deliver better models.

A prize is envisaged for the top performing approaches (2500EU in 
total). The price is sponsored by Rapid-I the company that supports
RapidMiner, probably the most popular open-source data mining 
environment, and the European Commission through the e-Lico EU project. 
Participants are expected to prepare a paper, maximum 8 pages, 
describing their approach. We plan to have a number of selected papers 
considered for publication in a special issue of a journal (to be 
announced soon).

Challenge web page: http://tunedit.org/challenge/ON .

Started: Sep 15, 2010
Ends: Dec 19, 2010

Organizing Committee:
- Alexandros Kalousis, University of Geneva, Switzerland
- Julie Klein, Inserm U858, Toulouse, France
- Joost Schanstra, Inserm U858, Toulouse, France
- Adam Woznica, University of Geneva, Switzerland

Adam Woznica

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