We aim to explore the computational needs to enable precision oncology through cancer panomics, and to encourage computational biologists in academia and industry to come together to tackle the hard problems to implement such a vision. We seek original contributions that discuss new methods or algorithms/infrastructure able to integrate two or more “omics” and/or clinical data types aimed to enable precision medicine in individual cancer patients (as opposed to pattern finding algorithms for cross-sectional studies). Submission could also formally pose a novel problem in cancer integrative analysis that the community will need to address, but more often will describe algorithms, models, and original solutions to a specific data integration analysis problem in cancer.
- Integrative analysis of high-throughput "omics" data from related samples or data types
- Pathway disruption analysis by combining data from different "omics" sources in single patients
- Joint analysis of "omics" data, literature, clinical trial data, and medical records
- Data structures & systems to enable big-data integrative analysis in patients
Søren Brunak, Ph.D.Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark
Francisco M. De La Vega, D.Sc.Annai Systems, Inc., Burlingame, CA
Adam Margolin, Ph.D.Oregon Health & Science University, Portland, OR
Ben J. Raphael, Ph.D.Brown University, Providence, RI
Gunnar Rätsch, Ph.D.Memorial Sloan-Kettering Cancer Center, NY, NY
Joshua M. Stuart, Ph.D.Center for Biomolecular Science and Engineering. University of California Santa Cruz.