Staunton, J. E., Slonim, D. K., Coller, H. A., Tamayo, P., Angelo, M. J., Park, J., Scherf, U., Lee, J. K., Reinhold, W. O., Weinstein, J. N., Mesirov, J. P., Lander, E. S., and Golub, T. R., (2001) Chemosensitivity prediction by transcriptional profiling, PNAS, 98, 10787?10792.

has been cited by the following article:

• TITLE: Correlation of selected molecular markers in chemosensitivity prediction

  AUTHORS: David King, Thomas Keane, Wei Hu

  KEYWORDS: Cancer; Chemosensitivity; Correlation; D’; Feature Selection; Genetic Algorithm; Markov Blanket; Memetic Algorithm; NCI-60

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.7, November 20, 2009

  ABSTRACT: Finding effective cancer treatment is a challenge, because the sensitivity of the cancer stems from both intrinsic cellular properties and acquired resistances from prior treatment. Previous research has revealed individual protein markers that are significant to chemosensitivity prediction. Our goal is to find correlated protein markers which are collectively significant to chemosensitivity prediction to complement the individual markers already reported. In order to do this, we used the D’ correlation measurement to study the feature selection correlations for chemosensitivity prediction of 118 anticancer agents with putatively known mechanisms of action. Three data-sets on the NCI-60 were utilized in this study: two protein datasets, one previously studied for chemosensitivity prediction and another novel to this topic, and one DNA copy number dataset. To validate our approach, we identified the protein markers that were strongly correlated by our analysis with the individual protein markers found in previous studies. Our feature analysis discovered highly correlated protein marker pairs, based on which we found individual protein markers with medical significance. While some of the markers uncovered were consistent with those previously reported, others were original to this work. Using these marker pairs we were able to further correlate the cellular functions associated with them. As an exploratory analysis, we discovered feature selection correlation patterns between and within different drug mechanisms of action for each of our datasets. In conclusion, the highly correlated protein marker pairs as well as their functions found by our feature analysis are validated by previous studies, and are shown to be medically significant, demonstrating D’ as an effective measurement of correlation in the context of feature selection for the first time.