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Träfflista för sökning "L773:0219 7200 OR L773:1757 6334 srt2:(2005-2009)"

Sökning: L773:0219 7200 OR L773:1757 6334 > (2005-2009)

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1.
  • Gamalielsson, Jonas, et al. (författare)
  • Gene Ontology-based Semantic Alignment of Biological Pathways by Evolutionary Search
  • 2008
  • Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific Publishing. - 0219-7200 .- 1757-6334. ; 6:4, s. 825-842
  • Tidskriftsartikel (refereegranskat)abstract
    • A large number of biological pathways have been elucidated recently, and there is a need for methods to analyze these pathways. One class of methods compares pathways semantically in order to discover parts that are evolutionarily conserved between species or to discover intraspecies similarities. Such methods usually require that the topologies of the pathways being compared are known, i.e. that a query pathway is being aligned to a model pathway. However, sometimes the query only consists of an unordered set of gene products. Previous methods for mapping sets of gene products onto known pathways have not been based on semantic comparison of gene products using ontologies or other abstraction hierarchies. Therefore, we here propose an approach that uses a similarity function defined in Gene Ontology (GO) terms to find semantic alignments when comparing paths in biological pathways where the nodes are gene products. A known pathway graph is used as a model, and an evolutionary algorithm (EA) is used to evolve putative paths from a set of experimentally determined gene products. The method uses a measure of GO term similarity to calculate a match score between gene products, and the fitness value of each candidate path alignment is derived from these match scores. A statistical test is used to assess the significance of evolved alignments. The performance of the method has been tested using regulatory pathways for S. cerevisiae and M. musculus.
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2.
  • Olsson, Björn, et al. (författare)
  • Deriving pathway maps from automated text analysis using a grammar-based approach
  • 2006
  • Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific. - 0219-7200 .- 1757-6334. ; 4:2, s. 483-501
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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3.
  • Rantanen, Ville-Veikko, et al. (författare)
  • A Priori Contact Preferences in Molecular Recognition
  • 2005
  • Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 3:4, s. 861-890
  • Tidskriftsartikel (refereegranskat)abstract
    • A molecular interaction library modeling favorable non-bonded interactions between atoms and molecular fragments is considered. In this paper, we represent the structure of the interaction library by a network diagram, which demonstrates that the underlying prediction model obtained for a molecular fragment is multi-layered. We clustered the molecular fragments into four groups by analyzing the pairwise distances between the molecular fragments. The distances are represented as an unrooted tree, in which the molecular fragments fall into four groups according to their function. For each fragment group, we modeled a group-specific a priori distribution with a Dirichlet distribution.
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