Before revision:  These similarity values, rather than the original high dimensional feature data, are used to train kernelized machine learning algorithms such as support vector machines, thus handling the curse-of-dimensionality that typically affects genomic machine learning. Using four synthetic datasets of complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.
After revision:  These similarity values, rather than the original high dimensional feature data, are used to build the kernel. Results: We demonstrate and explore the simulation based kernel (SimKern) concept using four synthetic complex systems--three biological models and one network flow optimization model--we demonstrate that when the number of training samples is small compared to the number of features, the simulation kernel approach dominates over no-prior-knowledge methods.