Markku Siermala | PubMed Health

PubMed2006

Techniques for biased data distributions and variable classification with neural networks applied to otoneurological data.

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 distributions, a method to measure the classification information incorporated in variables and variable groups, and methods to express properties learned by a network from its structure. We tested these techniques with otoneurological data from the conjunction with vertiginous diseases that we have explored in our previous neural network studies.

Bias↗

PubMed2005

Dynamic covariation between gene expression and proteome characteristics.

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 identify proteome-wide characteristics indicative of trends common to expressed proteins. RESULTS: Numerous bioinformatics methods were used to filter and merge information regarding gene and protein annotations. A new statistical time point-oriented analysis was developed for the study of dynamic correlations in large time series data. The method was applied to investigate microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle. CONCLUSION: We show that the properties of proteins synthesized correlate dynamically with the gene expression profile, indicating that not only is the actual identity and function of expressed proteins important for cellular responses but that several physicochemical and other protein properties correlate with gene expression as well. Gene expression correlates strongly with amino acid composition, composition- and sequence-derived variables, functional, structural, localization and gene ontology parameters. Thus, our results suggest that a dynamic relationship exists between proteome properties and gene expression in many biological systems, and therefore this relationship is fundamental to understanding cellular mechanisms in health and disease.

Animals↗