344 Fendler and Atwal: Systematic deciphering of cancer genome networks gorithms such as the James-Stein shrinkage estimator and graphical lasso [36,37]. One difficulty, however, from this approach is that directed graphs are not generated (who causes who is unknown), as modified Bayesian networks can, such as BANJO [33]. Ultimately, however, any underlying network discovered with strong interactions can only be validated through further exper- imentation (Figure 1), use of protein-protein interaction databases, pathway interroga- tions, and utilizing other genomic based technologies. c o n c lu Sio n S Building gene networks from existing data is a bottom-up approach attempting to fill in the gaps and understand gene relations. While many network algorithms calculate sta- tistical correlations between genes, they often do not describe direct causal gene interactions, which are the explicit biological model we hope to capture. Improving these computa- tional methods is likely to be the future of re- verse-engineering gene networks, and here we have highlighted some promising approaches that have borrowed concepts from statistical physics. In addition, since fundamentally dif- ferent biochemical genomic technologies rep- resent different observables, for example, ChIP-seq [42], combining them with other measurables will likely lead to dimension re- duction, improving both gene candidate false positives as well as reduce noise in gene inter- action networks. Finally, other non-genomic- based technologies, such as protein-protein interaction [43] and pathway [44] databases, have been curated, which may be used to sup- plement these investigations as well as validate discovered networks. It is the hope of cancer biology that this data may be integrated into a complete model defining the cancer genome. r e Fe r e n c e S 1. American Cancer Society. Global Cancer Facts & Figures 2nd Edition. Atlanta: Amer- ican Cancer Society. 2011. 2. Doll R, Peto R. The causes of cancer: quan- titative estimates of avoidable risks of cancer in the United States today. J Natl Cancer Inst. 1981;66(6):1191-308. 3. Peto J. Cancer epidemiology in the last cen- tury and the next decade. Nature. 2011;411(6835):390-5. 4. Reich DE, Lander ES. On the allelic spec- trum of human disease. Trends Genet. 2001;17(9):502-10. 5. Wang WY, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet. 2005;6(2):109-18. 6. Balmain A, Gray J, Ponder B. The genetics and genomics of cancer. Nat Genet. 2003;33 (Suppl):238-44. 7. Johnson GC, Todd JA. Strategies in complex disease mapping. Curr Opin Genet Dev. 2000;10(3):330-4. 8. Malkin D, Li FP, Strong LC, Fraumeni JF, Jr., Nelson CE, Kim DH, et al. Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Sci- ence. 1990;250(4985):1233-8. 9. Talbot SJ, Crawford DH. Viruses and tu- mours―an update. Eur J Cancer. 2004;40(13):1998-2005. 10. Camp RL, Neumeister V, Rimm DL. A decade of tissue microarrays: progress in the discovery and validation of cancer biomark- ers. J Clin Oncol. 2008;26(34):5630-7. 11. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, et al. Genome sequenc- ing in microfabricated high-density picolitre re- actors. Nature. 2005;437(7057):376-80. 12. Mardis ER. Next-generation DNA sequenc- ing methods. Annu Rev Genomics Hum Genet. 2008;9:387-402. 13. von Bubnoff A. Next-generation sequencing: the race is on. Cell. 2008;132(5):721-3. 14. Guo J, Yu L, Turro NJ, Ju J. An integrated system for DNA sequencing by synthesis using novel nucleotide analogues. Acc Chem Res. 2010;43(4):551-63. 15. Metzker ML. Sequencing technologies ― the next generation. Nat Rev Genet. 2010;11(1):31- 46. 16. DNA Sequencing Costs: Data from the NHGRI Large-Scale Genome Sequencing Program. Genome.com [Internet]. Available from: www.genome.gov/sequencingcosts. 17. Hurd PJ, Nelson CJ. Advantages of next-gen- eration sequencing versus the microarray in epigenetic research. Brief Funct Genomic Proteomic. 2009;8(3):174-83. 18. Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, et al. Recurring mu- tations found by sequencing an acute myeloid leukemia genome. N Engl J Med. 2009;361(11):1058-66. 19. Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature. 2010;464(7291):999-1005. 20. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57-63.