Markku Siermala

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Polyproline type II stretches are somewhat rare on proteins. The backbone of this secondary structural element folds to a triangular form instead of the normal alpha-helix with 3.6 residues per turn. It is a very challenging task to try to detect them computationally from protein sequence. Here, we have studied the preprocessing phase in particular, which(More)
BACKGROUND The immune system, which is a complex machinery, is based on the highly coordinated expression of a wide array of genes and proteins. The evolutionary history of the human immune system is not well characterised. Although several studies related to the development and evolution of immunological processes have been published, a full-scale(More)
BACKGROUND Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to changes in gene and protein expression. Since the majority of genes encode proteins, we investigated possible correlations between protein parameters and gene expression patterns to(More)
This study considers detection of polyproline type II secondary structures from protein sequences. This difficult problem was handled with multilayer perceptron neural networks, which were found to be useful for such bioinformatics studies. Polyproline II secondary structures have not previously been tried to be predicted from sequences.
It is frequently useful and advantageous to investigate not only the classification efficacy of neural networks, but also the reasons for misclassification and relations between input variables and output classes. We have developed novel techniques to disentangle these dilemmas: a network structure and learning strategy for biased output class(More)
When complex data is distributed in a biased manner between disease classes, classification accuracy can be increased with a network set of perceptron neural networks developed by us. A novel projection method is also introduced for the visual classification of the data to elucidate its features and disease class distribution. The set of the perceptron(More)