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- Gustafsson, Mika, 1977-, et al.
(author)
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Comparison and validation of community structures in complex networks
- 2006
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In: Physica A. - : Elsevier BV. - 0378-4371 .- 1873-2119. ; 367, s. 559-576
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Journal article (peer-reviewed)abstract
- The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information.Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.
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| 2. |
- Gustafsson, Mika, 1977-, et al.
(author)
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Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
- 2005
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In: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - 1545-5963 .- 1557-9964. ; 2:3, s. 254-261
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Journal article (peer-reviewed)abstract
- We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.
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| 5. |
- Gustafsson, Mika, 1977-
(author)
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Large-scale topology, stability and biology of gene networks
- 2006
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Licentiate thesis (other academic/artistic)abstract
- Experimental innovations in cell biology have provided a huge amount of genomescale data sets, settling the stage for understanding organisms on a system level. Recently, complex networks have been widely adopted and serve as a unifying language for widely different systems, including social, technological and biological systems. Still- in most biological cases-the number of interacting units vastly exceeds the number of measurements, hence large-scale models must still be very simple or non-specific. In this thesis we explore the limits of a linear (Lasso) network model on a genomic-scale for the Saccharomyces cerevisae organism and the limits of some analysis tools from the research field of complex networks. The former study (Paper I and Paper III) mainly regards validation issues, but also stipulate novel statistical system hypotheses, e.g., the system is significantly more stable than random, but still flexible to target stimuli. The latter study (Paper II) explores different heuristics in the search for communities (i.e., dense sub-graphs) within large complex networks and how the concept relates to functional modules.
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