Machine learning in immunology competition


Task Description...

Schedule...

Competition Rules...

MLI Registration...


We are pleased to announce the machine learning in immunology (MLI) competition. Machine learning applications in immunology play a key role in the field of immunoinformatics.

Experimental studies of immune system and related applications such as vaccine design, optimization of therapies, or other combinatorially complex applications. These experimental approaches are time-consuming and expensive. New approaches involve pre-screening by computational models, followed by experimental validation using selected key experiments.

Peptides that bind Major Histocompatibility Complex Molecules (MHC) are important targets in studies of cell-mediated immunity, regulation of immune responses, vaccine research, and transplant rejection. A number of computational models have been developed for prediction of MHC-binding peptides (see Brusic et al, 2004) and standards for their assessments have been developed (see Peters et al, 2006; Gowthaman and Agrewala, 2008; Wang et al., 2008; Lundegaard et al, 2008; Lin et al, 2008a; Lin et al, 2008b). Some of these models are highly accurate, while others need improvement. Machine learning-based methods offer a great promise for further advancement of prediction systems in this field.

Participants in this competition will develop prediction methods using available data sets. Such sets are available from various databases such as IEDB, SYFPEITHI, MHCPEP, ANTIJEN, and MHCBN. Organized data sets are available at the DFRMLI site. The competitors will be presented with new sets of experimentally identified target data and asked to perform predictions. The groups that demonstrate best predictive performance on these carefully selected data sets will receive the ICANN’09 MLI competition prize and certificates. The predictive performance will be assessed in accordance to criteria defined in Lin et al, 2008a.

This competition will help identify machine learning methods that will improve upon currently available tools for prediction of peptide binding to MHC molecules. In particular we invite participants from mainstream machine learning community to join the competition and help address this challenging problem, traditionally done by bioinformaticians.

Vladimir Brusic and Wlodzislaw Duch
April 21, 2009

Task Description

The target of competition will be prediction of peptide binding to a subset of HLA class I molecules. The list of target peptides will be 9- and 10-mers and the target molecules will be three from the HLA set {A*0101, A*0201, A*0301, A*1101, A*2402, B*0702, B*0801, B*1501}.

Participation

The competition is open to any individual except for the organizers and their affiliates. The participants will remain anonymous unless they request for non-anonymous participation or if they are among the winners. Participants are not required to attend the Workshop, but are encouraged to do so. See Rules for further details.

Schedule

  DATE   STATUS DESCRIPTION
May 31Opened*Pre-registration
Jun 15 Registration opens
Jun 15 Practice data sets will be posted to the web site
Aug 15 Registration closes
COMPETITION
Sep 04 Target data made available
Sep 08 Deadline for submission of the prediction data
Sep 10 Deadline for submission of corrected prediction data
WORKSHOP AND PUBLICATION
Sep 17 ICANN 2009 Workshop
Sep 17 Announcement of winners and prize ceremony
Sep 16 Invitations for submission to publish
Nov 01 Papers ready for submission
*To preregister, send the following information to Vladimir Brusic:
  • Name
  • Institution
  • Contact email
  • Pseudonym for use in the competition
  • Competition Rules...