A method of analyzing DNA microarray data based on the physical modeling of hybridization is presented. We demonstrate, in experimental data, a correlation between observed hybridization intensity and calculated free energy of hybridization. Then, combining hybridization rate equations, calculated free energies of hybridization, and microarray data for known target concentrations, we construct an algorithm to compute transcript concentration levels from microarray data. We also develop a method for eliminating outlying data points identified by our algorithm. We test the efficacy of these methods by comparing our results with an existing statistical algorithm, as well as by performing a crossvalidation test on our model.

A theoretical study of the physical properties which determine the variation in signal strength from probe to probe on a microarray is presented. A model which incorporates probe-target hybridization, as well as the subsequent dissociation which occurs during stringent washing of the microarray, is introduced and shown to reasonably describe publicly available spike-in experiments carried out at Affymetrix. In particular, this model suggests that probe-target dissociation during the stringent wash plays a critical role in determining the observed hybridization intensities. In addition, it is demonstrated that non-specific hybridization introduces uncertainties which significantly limit the ability of any model to accurately quantify absolute gene expression levels while, in contrast, target folding appears to have little effect on these results. Finally, for data from target spike-in experiments, our model is shown to compare favorably with an existing statistical model in determining target concentration levels.

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Information Visualization (InfoVis) is now an accepted and growing field but questions remain about the best uses for and the maturity of novel visualizations. Usability studies and controlled experiments are helpful but generalization is difficult. We believe that the systematic development of benchmarks will facilitate the comparison of techniques and help identify their strengths under different conditions. We were involved in the organization and management of three information visualization contests for the 2003, 2004 and 2005 IEEE Information Visualization Symposia, which requested teams to report on insights gained while exploring data. We give a summary of the state of the art of evaluation in information visualization, describe the three contests, summarize their results, discuss outcomes and lessons learned, and conjecture the future of visualization contests. All materials produced by the contests are archived in the Information Visualization Benchmark Repository.