The main advantage of this algorithm lays in its multi-factorial consideration of each input which allows the magnitude of interaction of a given pair of parameters to be determined on the basis of a matrix of full interaction, and by iteratively examining the weights and prediction performance of each single input expression from all the others within the set. Despite the obvious advantages of using well-characterised simulated datasets for the testing of new analysis tools, it is important to note that human biological data are complex and the lack in the knowledge of actual biological Anacetrapib correlation between sample replicates, molecular relationship between a biological state of a cell and transcript expression, biochemical reaction mechanisms underlying regulatory interactions between features and activity changes from one state to another. This makes artificial data valuable for algorithm development, but is not of value for comparing different methods. To assess the predictive ability of the algorithm, criteria such as number of hidden nodes used in the network, correlation analysis comparing the predicted correlation scores for each pair of the features with their actual correlation values, interaction signs analysis comparing between the sign of the actual correlation value and the sign of the predicted interaction score and true positive rate have been considered. Table 3 shows a summary of the results. High accuracy on the TPR, correlation result and predicted interaction sign confirm the feasibility of this approach to accurately identify the simulated features having strong correlations. In terms of network architecture, there is no significant improvement on TPR when the number of hidden nodes increases, thereby suggesting that the number of hidden nodes does not affect the predictive ability of the algorithm. A model with 2 hidden nodes performs equally good, or better than those equipped with higher number of hidden nodes and lesser computational time is needed to process the query. Thus, 2 hidden nodes were implemented in the algorithm. A full, comprehensive empirical validation on the algorithm can be found in Lemetre��s PhD Thesis. RMS and EWS are soft tissue sarcomas that can be found virtually anywhere in the body and share Phthalylsulfacetamide common clinical characteristics, more frequently occurring in males than females and normally found in children.