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German Conference on Bioinformatics

https://dl.gi.de/handle/20.500.12116/21203
Auflistung nach:

Auflistung German Conference on Bioinformatics nach Autor:in "Antonov, Alexey V."

1 - 3 von 3
  • Konferenzbeitrag
    Consecutive KEGG pathway models for the interpretation of high-throughput genomics data
    (German Conference on Bioinformatics, 2008) Antonov, Alexey V.; Diemann, Sabine; Mewes, Han W.
    A common strategy to deal with the interpretation of gene lists is to look for overrepresentation of Gene Ontology (GO) terms or pathways. In related computational approaches the cell is formalized as genes that are grouped into functional categories. As output, a list of interesting biological processes is provided, which seems to be mostly covered by the supplied gene list. However, it is more natural to model the cell as a network that reflects relations between genes. For many biological processes such information is available, but it is not used to the full extent in interpretational analyses. In this paper, we propose to interpret gene lists in network terms to provide the most probable scenario of gene interactions based on the available information about the topology of metabolic pathways. The proposed approach is an effort to exploit the biological information available in public resources to a greater extent in comparison to the existing techniques. Applying our approach to experimental data, we demonstrate that the currently widely employed strategy produces an incomplete interpretation, whilst our procedure provides deeper insights into possible molecular mechanisms behind the experimental data.
  • Konferenzbeitrag
    Exploiting scale-free information from expression data for cancer classification
    (German Conference on Bioinformatics 2005 (GCB 2005), 2005) Antonov, Alexey V.; Tetko, Igor V.; Kosykh, Denis; Surmeli, Dmitrij; Mewes, Hans-Werner
    In most studies concerning expression data analyses information on the variability of gene intensity across samples is usually exploited. This information is sensitive to initial data processing which affects the final conclusions. However expression data contains scale free information which is directly comparable between different samples. We propose to use the pairwise ratio of gene expression values rather than their absolute intensities for classification of expression data. This information is stable to data processing and thus more attractive for classification analyses. In proposed schema of data analyses only information on relative gene expression levels in each sample is exploited. Testing on publicly available datasets leads to superior classification results.
  • Konferenzbeitrag
    Revealing comprehensive genotype–phenotype associations through logic relationships
    (German conference on bioinformatics – GCB 2007, 2007) Antonov, Alexey V.; Mewes, Hans W.
    A novel approach which employs principles of higher order logic analyses was developed to systematically correlate phylogenetic data with phenotype profiles by identification of phenotype specific patterns of presence of multiple proteins. For example, for most genomes expressing trait A, the presence of protein C presumes the presence of protein B, while for other genomes (not expressing the trait) the presence of protein C presumes the absence of protein B. We demonstrate that the phenotype specific patterns reflect fundamental structural changes in the genotype of microorganisms in relation to conditions provided by presence/absence of a trait. We discover many previously unidentified genotype–phenotype associations on the level of fundamental biochemical processes.