Sandpit on the Application of Machine Learning Techniques to the Analysis of Complex Biomedical Data

Sandpit on the Application of Machine Learning Techniques to the Analysis of Complex Biomedical Data

Posted by Rebecca Martin on Mon, 08/06/2009 - 15:18

Joint National Institute of Medical Research UK, University College London, PASCAL Workshop:

Sandpit on the Application of Machine Learning Techniques to the Analysis of Complex Biomedical Data
6-7th July, 2009
National Institute for Medical Research,
London

URL: http://www.davidroihardoon.com/SCBD09/

Organisers: Delmiro Fernandez-Reyes, James Briscoe, Gunnar Raetsch, John Shawe-Taylor, David R. Hardoon

This workshop will bring together a group of researchers from the biomedical sciences, machine learning and computational biology, to address the solution of challenging problems and help to develop an edge over existing approaches. The workshop will be organized around a small set of biomedical researchers, who will describe datasets and associated analysis challenges. At the same time there will be an opportunity for machine learning researchers to give overviews of approaches that might be effective for these problems.

At the least we hope that the interchange will provide valuable insights for researchers from both communities into the problems and techniques. The ideal outcome would be the establishment of new cross-disciplinary collaborations and/or new challenges based on the biomedical data.

The workshop will be grouped around four themes each occupying half a day.

Theme 1: Sequence Analysis, Motifs and Signal Detection
Main presenters:

* Sebastien Gagneux, “Next-generation sequencing for the population genomics and epidemiology of the tubercle bacilli”
* James Briscoe TBA

Theme 2: Gene and Protein Expression Analysis
Main presenters:

* Diogo Castro, “Molecular Neurobiology Identifying the genome-wide targets of transcription factor Mash1”
* Ben Martynoga, “Molecular Neurobiology Genome-wide location of transcription factors that share common targets in neural stem cells”
* Robert Wilkinson, “Bioinformatic and empirical analysis of novel hypoxia-induced antigenic targets in M. tuberculosis”
* Anne O’Garra TBA

Theme 3: Systems Biology and Medicine
Main presenters:

* Ben Seddon, “Immune Cell Biology Genetic networks regulated by IL7 in T cells”
* Thea Hogan, “Immune Cell Biology Modelling proliferation, survival and differentiation in T cell homeostasis”
* Delmiro Fernandez-Reyes TBA

Theme 4: Bio-imaging

* Peter Rosenthal, “Computing Cellular Architecture Systems Biology”

We welcome contributions of short presentations of approximately in areas of Machine learning addressing the four themes in order to promote the interaction. These could include but are not limited to:

* Statistical inference in High Dimensional Spaces
* Unsupervised, Semi-Supervised and Supervised Learning
* Variable and Feature Selection
* Clustering
* Structured output learning
* Graph and manifold learning

Please submit your proposed contribution to email sandpit@cs.ucl.ac.uk before June 21.