Cancer Bioinformatics Workshop

3 September 2010, 9.00 AM - 3 September 2010, 9.00 AM

Workshop organisers: Colin Campbell (University of Bristol, UK) | Christina Leslie (Sloan Kettering Cancer Center, NY, USA) | Florian Markowetz (Cancer Research UK Cambridge Research Institute, UK) | Jean-Philippe Vert (Institut Curie, Paris, France)
Programme | Associated edited volume | Invited speakers and sponsors

This workshop was held 2 - 4 September 2010 with the following final programme (PDF, 196kB). Some of the talks can be seen at the corresponding website.

Substantial amounts of data are being generated within cancer research. Datasets range from gene expression and microRNA array data through to next generation sequence data. Data interpretation draws on mathematical and computational skills and thus the subject has engaged the interest of researchers in areas such as machine learning, statistics, bioinformatics and computer science. The goal of this cross-disciplinary Workshop is therefore to bring together researchers from these disciplines and cancer researchers who have an interest in data analysis, to explore and present innovative approaches to this subject. Presented papers should:

  1. Propose novel data analysis methods applicable to this domain or:
  2. Present bioinformatics-driven studies in which mathematical or computational methods played an important role in finding results of potential significance in cancer research.

For novel data analysis methods, a non-exhaustive list of suitable topics include:

  • Unsupervised, semi-supervised and biclustering methods to highlight disease subtypes or dysregulated genes within these subtypes
  • Data integration/data fusion methods to integrate different types of data such as gene expression, microRNA expression and array CGH data
  • Inference of gene regulatory networks
  • Pathway modeling and probabilistic ranking of pathway models
  • Biomarker discovery
  • Genome-wide association studies
  • Rational drug design methods and chemoinformatics
  • Protein function, structure prediction and structural bioinformatics
  • microRNA target site prediction
  • Analysis of high throughput sequencing data
  • Gene expression and post-transcriptional regulation
  • Methods for the detection of fusion genes
  • Prediction of disease progression
  • Probabilistic inference, Bayesian methods and Kernel-based methods for classifier design with applications to cancer bioinformatics
  • Methods for the detection and quantification of copy number alterations and deletions

The workshop is principally focused on the intepretation of omics datasets and does not cover related areas such as cancer imaging or development of software tools unless in the context of novel methodology. There is a planned associated edited volume.