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  • 1.
    Lindlöf, Angelica
    et al.
    University of Skövde, School of Humanities and Informatics.
    Lubovac, Zelmina
    University of Skövde, School of Humanities and Informatics.
    Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks2005In: In Silico Biology, ISSN 1386-6338, Vol. 5, no 3, p. 239-249Article in journal (Refereed)
    Abstract [en]

    Recent research on large scale microarray analysis has explored the use of Relevance Networks to find networks of genes that are associated to each other in gene expression data. In this work, we compare Relevance Networks with other types of clustering methods to test some of the stated advantages of this method. The dataset we used consists of artificial time series of Boolean gene expression values, with the aim of mimicking microarray data, generated from simple artificial genetic networks. By using this dataset, we could not confirm that Relevance Networks based on mutual information perform better than Relevance Networks based on Pearson correlation, partitional clustering or hierarchical clustering, since the results from all methods were very similar. However, all three methods successfully revealed the subsets of co-expressed genes, which is a valuable step in identifying co-regulation.

  • 2.
    Synnergren, Jane
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Gamalielsson, Jonas
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Classification of information fusion methods in systems biology2009In: In Silico Biology, ISSN 1386-6338, Vol. 9, no 3, p. 65-76Article, review/survey (Refereed)
    Abstract [en]

    Biological systems are extremely complex and often involve thousands of interacting components. Despite all efforts, many complex biological systems are still poorly understood. However, over the past few years high-throughput technologies have generated large amounts of biological data, now requiring advanced bioinformatic algorithms for interpretation into valuable biological information. Due to these high-throughput technologies, the study of biological systems has evolved from focusing on single components (e.g. genes) to encompassing large sets of components (e.g. all genes in an entire genome), with the aim to elucidate their interdependences in various biological processes. In addition, there is also an increasing need for integrative analysis, where knowledge about the biological system is derived by data fusion, using heterogeneous data sets as input. We here review representative examples of bioinformatic methods for fusion-oriented interpretation of multiple heterogeneous biological data, and propose a classification into three categories of tasks that they address: data extraction, data integration and data fusion. The aim of this classification is to facilitate the exchange of methods between systems biology and other information fusion application areas.

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