Article: Predicting cancer drivers: are we there yet?
Saturday, December 1st, 2012http://genomemedicine.com/content/4/11/88
associated with transFIC method
http://genomemedicine.com/content/4/11/88
associated with transFIC method
This paper introduces a new method for detecting copy number variants in cancer genomes that addresses deficiencies of previous detection methods. The new method, dubbed HHCRF by the authors, adds the use of sequential correlations in selecting classification features for inferring copy numbers and identifying clinically relevant genes. This improvement results in higher accuracy on noisy data, and the identification of more clinically relevant genes, relative to previous methods. These results were obtained by testing HHCRF on both simulated array-CGH microarray data, and on actual breast cancer, uveal melanoma, and bladder tumor datasets.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2677736/
Bioinformatics. 2009 May 15;25(10):1307-13. Epub 2008 Dec 3. Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields.
Barutcuoglu Z, Airoldi EM, Dumeaux V, Schapire RE, Troyanskaya OG.
– Very much argues the dirt is good case
http://www.newyorker.com/reporting/2012/10/22/121022fa_fact_specter
From XJM:
A few references about nonsynonymous/synonymous ratio in Cancer: Here is a Nature paper finding nonsynonymous/synonymous ratio to be 3:1 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712719/
Here is an article reporting the ratio to be about 4:1
http://www.nature.com/ng/journal/v43/n11/full/ng.950.html
Another one:
http://onlinelibrary.wiley.com/doi/10.1111/j.1755-148X.2012.00976.x/full
An online powerpoint reporting 2:1 ratio:
http://www.genome.gov/Pages/Research/DIR/DIRNewsFeatures/Next-Gen101/Samuels_WholeExomeSequencing.pdf
http://www.pnas.org/content/109/35/14087.abstract
Allusion to whole genome data, but focus is on coding regions & mitochondrial mutations