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Per Kristian Lehre

Publications and source records attributed to Per Kristian Lehre.

3 recordsLinked to original sources

The genotypic complexity of evolved fault-tolerant and noise-robust circuits.

Noise and component failure is an increasingly difficult problem in modern electronic design. Bio-inspired techniques is one approach that is applied in an effort to solve such issues, motivated by the strong robustness and adaptivity often observed in nature. Circuits investigated herein are designed to be tolerant to faults or robust to noise, using an evolutionary algorithm. A major challenge is to improve the scalability of the approach. Earlier results have indicated that the evolved circuits may be suited for the application of artificial development, an approach to indirect mapping from genotype to phenotype that may improve scalability. Those observations were based on the genotypic complexity of evolved circuits. Herein, we measure the genotypic complexity of circuits evolved for tolerance to faults or noise, in order to uncover how that tolerance affects the complexity of the circuits. The complexity is analysed and discussed with regards to how it relates to the potential benefits to the evolutionary process of introducing an indirect genotype-phenotype mapping such as artificial development.

Algorithms↗

Phenotypic complexity and local variations in neutral degree.

Neutrality is important in natural, molecular and artificial evolution. This work studies how local neutral degree varies over the genospace in a simple class of Lindenmayer-systems, and investigates whether this variation relates to Lempel-Ziv complexity of the phenotype.

Biological Evolution↗

Aetiology-specific patterns in end-stage heart failure patients identified by functional annotation and classification of microarray data.

BACKGROUND: The objective of the present study was to use gene expression profiling, functional annotations and classification to identify aetiology-specific biological processes and potential molecular markers for different aetiologies of end-stage heart failure. METHODS AND RESULTS: Individual left ventricular myocardial samples from eleven coronary artery disease and nine dilated cardiomyopathy transplant patients were co-hybridized with pooled RNA from four non-failing hearts on custom-made arrays of 7000 human genes. Significance analysis identified differential expression of 153 and 147 genes, respectively, in coronary artery disease or dilated cardiomyopathy versus non-failing hearts. Analysis of Gene Ontology biological process annotations indicated aetiology-specific patterns, primarily related to genes involved in catabolism and in regulation of protein kinase activity. Gene expression classifiers were obtained and used for class prediction of random samples of coronary artery diseased and dilated cardiomyopathic hearts. Best classifiers frequently included matrix metalloproteinase 3, fibulin 1, ATP-binding cassette, sub-family B member 1 and iroquois homeobox protein 5. CONCLUSION: Combining functional annotation from microarray data and classification analysis constitutes a potent strategy to identify disease-specific biological processes and gene expression markers in e.g. end-stage coronary artery disease and dilated cardiomyopathy.

Adult↗