Cancer biology is undergoing a revolution driven by the application of high-throughput techniques such as genome sequencing, comparative genomic hybridization, expression microarrays, miRNA profiling, and mass spectrometry to tumor samples. These techniques give rise to large collections of data that are impacting both basic cancer biology as well as clinical applications. Cancer is disease of tremendous complexity, and thus the analysis and interpretation of this data, taking a systems biology approach, demands sophisticated, specialized computational methods.
This workshop brings together leading researchers in the mathematical, computational and life sciences to discuss cutting edge cancer research. The emphasis of all contributed work will be on applying statistical and algorithmic approaches to improve our understanding of cancer and on the development of useful, effective and efficient new methods in this area.
Topics of interest include, but are not limited to
* Gene and protein expression data analysis in cancer
* Analysis of copy number variation
* Statistical genetics of cancer
* Biomarkers & cancer signatures
* Models of cancer progression or development
* The role of miRNA and other non coding RNAs in cancer
* Computational epigentics
* Inference of cancer networks e.g. signaling networks
* Integrative approaches to understanding cancer