Biomedical subjects

S Harbeck

Publications and source records attributed to S Harbeck.

3 recordsLinked to original sources

PubMed2001

Preparation and certification of a reference material on PCBs in pig fat and its application in quality control in monitoring laboratories during the Belgian "PCB-crisis".

In this article, the production and validation of a new certified reference material "PCBs in animal fat" for the control of the maximum level of 200 ng/g setup by the European Communities for veterinary products from Belgium is described. Three materials are established: a blank, one material with about 100 ng/g and one with about 200 ng/g (sum of seven PCBs). Data on the production and certification are given. Additionally, this material was used as an unknown test material in the quality assurance program of the Belgium meat monitoring system (before the certification of the material). While the certification was performed with an uncertainty of less than 10%, the round robin exhibited larger deviations. However, these deviations were less than 20% for most of the 30 participating laboratories. Only two had significantly higher deviations.

Adipose Tissue↗

PubMed2000

Stochastic segment models of eukaryotic promoter regions.

We present a new statistical approach for eukaryotic polymerase II promoter recognition. We apply stochastic segment models in which each state represents a functional part of the promoter. The segments are trained in an unsupervised way. We compare segment models with three and five states with our previous system which modeled the promoters as a whole, i.e. as a single state. Results on the classification of a representative collection of human and D. melanogaster promoter and non-promoter sequences show great improvements. The practical importance is demonstrated on the mining of large contiguous sequences.

Algorithms↗

PubMed1999

Interpolated markov chains for eukaryotic promoter recognition.

MOTIVATION: We describe a new content-based approach for the detection of promoter regions of eukaryotic protein encoding genes. Our system is based on three interpolated Markov chains (IMCs) of different order which are trained on coding, non-coding and promoter sequences. It was recently shown that the interpolation of Markov chains leads to stable parameters and improves on the results in microbial gene finding (Salzberg et al., Nucleic Acids Res., 26, 544-548, 1998). Here, we present new methods for an automated estimation of optimal interpolation parameters and show how the IMCs can be applied to detect promoters in contiguous DNA sequences. Our interpolation approach can also be employed to obtain a reliable scoring function for human coding DNA regions, and the trained models can easily be incorporated in the general framework for gene recognition systems. RESULTS: A 5-fold cross-validation evaluation of our IMC approach on a representative sequence set yielded a mean correlation coefficient of 0.84 (promoter versus coding sequences) and 0.53 (promoter versus non-coding sequences). Applied to the task of eukaryotic promoter region identification in genomic DNA sequences, our classifier identifies 50% of the promoter regions in the sequences used in the most recent review and comparison by Fickett and Hatzigeorgiou ( Genome Res., 7, 861-878, 1997), while having a false-positive rate of 1/849 bp.

Algorithms↗