Thus, when the actual degree of hybridization for the entire data set (265 probe-target combinations) was matched with the predicted ÁG in Visual Omp TM 7.0, an only moderately precise correlation was obtained. The hybridization of combinations involving many mismatches and many dInosines was poorly predicted. However, when the NucZip algorithm was used, a higher degree of correlation was observed. The adjusted determination coefficient (R a 2 ) was 0.8636, indicating that 87% of the variation was explained by the NucZip algorithm, while the best fit of MFI% to the Visual Omp TM 7.0 predictions gave a determination coefficient of 0.7505, indicating that 75% of the variation was explained by NN theory (as embodied in Visual Omp TM 7.0). NN theory was thus insufficient for predicting hybridization under the hybridization conditions of our study. A high number of mismatches and dInosines gave hybridization predictions in Visual Omp TM 7.0 that were too low ( Figure 6B ). The NucZip algorithm, which takes into account the length of matching segments and cooperativity effects within and between matching oligonucleotide segments, increased the accuracy of hybridization prediction. dInosines were scored intermediate between matches and mismatches.