Biomedical subjects

Björn Olsson

Publications and source records attributed to Björn Olsson.

8 recordsLinked to original sources

PubMed2007

Deriving evolutionary tree models of the oncogenesis of endometrial adenocarcinoma.

Endometrial adenocarcinoma (EAC) is the fourth leading cause of cancer death in women worldwide, but not much is known about the underlying genetic factors involved in the development of this complex disease. In the present work, we used 3 different algorithms to derive tree models of EAC oncogenesis from data on the frequencies of genomic alterations in rat chromosome 10 (RNO10). The tumor material was derived from progenies of crosses between the EAC susceptible BDII inbred rat strain and two non susceptible inbred rat strains. Data from allelic imbalance scans of RNO10 with microsatellite markers on solid tumor material and corresponding tissue cultures were used. For the analysis, RNO10 was divided into 24 segments containing a total of 59 informative microsatellite markers. The derived tree models show that genomic alterations have occurred in 11 of the 24 segments. In addition, the models provide information about the likely order of the alterations as well as their relationship with each other. Interestingly, there was a high degree of consistency among the different tree models and with the results of previous studies, which supports the reliability of the tree models. Our results may be extended into a general approach for tree modeling of whole genome alterations during oncogenesis.

Adenocarcinoma↗

PubMed2007

Chromosomal changes associated with clinical outcome in lymph node-negative breast cancer.

Breast cancer is the most common malignancy among women and accounts for over one million new cases worldwide per year. Lymph node-negative breast cancer patients are reputed as having a better prognosis than lymph node-positive ones. Around 20% of the lymph node-negative patients die within 10 years after diagnosis. To improve the prognostics of node-negative breast cancer, it is important to understand the underlying biologic mechanisms promoting survival, such as specific genetic changes in the tumor genome. In this study, CGH was applied to analyze 64 tumors from node-negative breast cancer patients to identify DNA copy number changes in chromosomes and chromosome regions that may be correlated to survival. The main findings show gains at 4q, 5q31 approximately qter, 6q12 approximately q16, and 12q14 approximately q22, as well as losses of 17p, 18p, and Xq, which were significantly more recurrent in tumors from deceased patients than in tumors from survivors. The average number of chromosomal changes was higher in the tumors from deceased compared to the survivor tumors. Our findings suggest that tumors with specific chromosomal aberrations at 4q, 5q31 approximately qter, 6q12 approximately q16, 12q14 approximately q22, 17p, 18p, and Xq result in an aggressive form of breast cancer and that these patients are predisposed to succumb to breast cancer.

Adult↗

PubMed2006

Combining functional and topological properties to identify core modules in protein interaction networks.

Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.

Algorithms↗

PubMed2006

Deriving pathway maps from automated text analysis using a grammar-based approach.

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

Abstracting and Indexing↗

PubMed2005

Generation and analysis of 9792 EST sequences from cold acclimated oat, Avena sativa.

BACKGROUND: Oat is an important crop in North America and northern Europe. In Scandinavia, yields are limited by the fact that oat cannot be used as a winter crop. In order to develop such a crop, more knowledge about mechanisms of cold tolerance in oat is required. RESULTS: From an oat cDNA library 9792 single-pass EST sequences were obtained. The library was prepared from pooled RNA samples isolated from leaves of four-week old Avena sativa (oat) plants incubated at +4 degrees C for 4, 8, 16 and 32 hours. Exclusion of sequences shorter than 100 bp resulted in 8508 high-quality ESTs with a mean length of 710.7 bp. Clustering and assembly identified a set of 2800 different transcripts denoted the Avena sativa cold induced UniGene set (AsCIUniGene set). Taking advantage of various tools and databases, putative functions were assigned to 1620 (58%) of these genes. Of the remaining 1180 unclassified sequences, 427 appeared to be oat-specific since they lacked any significant sequence similarity (Blast E values > 10(-10)) to any sequence available in the public databases. Of the 2800 UniGene sequences, 398 displayed significant homology (BlastX E values < or = 10(-10)) to genes previously reported to be involved in cold stress related processes. 107 novel oat transcription factors were also identified, out of which 51 were similar to genes previously shown to be cold induced. The CBF transcription factors have a major role in regulating cold acclimation. Four oat CBF sequences were found, belonging to the monocot cluster of DREB family ERF/AP2 domain proteins. Finally in the total EST sequence data (5.3 Mbp) approximately 400 potential SSRs were found, a frequency similar to what has previously been identified in Arabidopsis ESTs. CONCLUSION: The AsCIUniGene set will now be used to fabricate an oat biochip, to perform various expression studies with different oat cultivars incubated at varying temperatures, to generate molecular markers and provide tools for various genetic transformation experiments in oat. This will lead to a better understanding of the cellular biology of this important crop and will open up new ways to improve its agronomical properties.

Acclimatization↗

PubMed2005

Economic valuation for sustainable development in the Swedish coastal zone.

The Swedish coastal zone is a scene of conflicting interests about various goods and services provided by nature. Open-access conditions and the public nature of many services increase the difficulty in resolving these conflicts. "Sustainability" is a vague but widely accepted guideline for finding reasonable trade-offs between different interests. The UN view of sustainable development suggests that coastal zone management should aim at a sustainable ecological, economic, and social-cultural development. Looking closer at economic sustainability, it is observed that economic analyses about whether changes in society imply a gain or a loss should take into account the economic value of the environment. Methods used for making such economic valuation in the context of the Swedish coastal zone are briefly reviewed. It is noted that the property rights context matters for the results of a valuation study. This general background is followed by a concise presentation of the design and results of four valuation studies on Swedish coastal zone issues. One study is on the economic value of an improved bathing water quality in the Stockholm archipelago. The other studies are a travel cost study about the economic value of improved recreational fisheries in the Stockholm archipelago, a replacement cost study on the value of restoring habitats for sea trout, and a choice experiment study on the economic value of improved water quality along the Swedish westcoast.

Animals↗

PubMed2003

Genetic network inference: the effects of preprocessing.

Clustering of gene expression data and gene network inference from such data has been a major research topic in recent years. In clustering, pairwise measurements are performed when calculating the distance matrix upon which the clustering is based. Pairwise measurements can also be used for gene network inference, by deriving potential interactions above a certain correlation or distance threshold. Our experiments show how interaction networks derived by this simple approach exhibit low-but significant-sensitivity and specificity. We also explore the effects that normalization and prefiltering have on the results of methods for identifying interactions from expression data. Before derivation of interactions or clustering, preprocessing is often performed by applying normalization to rescale the expression profiles and prefiltering where genes that do not appear to contribute to regulation are removed. In this paper, different ways of normalizing in combination with different distance measurements are tested on both unfiltered and prefiltered data, different prefiltering criteria are considered.

Cluster Analysis↗

PubMed2002

Artificial intelligence techniques for bioinformatics.

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.

Algorithms↗