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Search Results: 1 - 10 of 223819 matches for " R. Toivonen "
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Anomalous lifetime distributions and topological traps in ordering dynamics
X. Castello,R. Toivonen,V. M. Eguiluz,J. Saramaki,K. Kaski,M. San Miguel
Physics , 2007, DOI: 10.1209/0295-5075/79/66006
Abstract: We address the role of community structure of an interaction network in ordering dynamics, as well as associated forms of metastability. We consider the voter and AB model dynamics in a network model which mimics social interactions. The AB model includes an intermediate state between the two excluding options of the voter model. For the voter model we find dynamical metastable disordered states with a characteristic mean lifetime. However, for the AB dynamics we find a power law distribution of the lifetime of metastable states, so that the mean lifetime is not representative of the dynamics. These trapped metastable states, which can order at all time scales, originate in the mesoscopic network structure.
Broad lifetime distributions for ordering dynamics in complex networks
R. Toivonen,X. Castelló,V. M. Eguíluz,J. Saram?ki,K. Kaski,M. San Miguel
Physics , 2008, DOI: 10.1103/PhysRevE.79.016109
Abstract: We search for conditions under which a characteristic time scale for ordering dynamics towards either of two absorbing states in a finite complex network of interactions does not exist. With this aim, we study random networks and networks with mesoscale community structure built up from randomly connected cliques. We find that large heterogeneity at the mesoscale level of the network appears to be a sufficient mechanism for the absence of a characteristic time for the dynamics. Such heterogeneity results in dynamical metastable states that survive at any time scale.
A Model for Social Networks
R. Toivonen,J. -P. Onnela,J. Saram?ki,J. Hyv?nen,K. Kaski
Physics , 2006, DOI: 10.1016/j.physa.2006.03.050
Abstract: Social networks are organized into communities with dense internal connections, giving rise to high values of the clustering coefficient. In addition, these networks have been observed to be assortative, i.e. highly connected vertices tend to connect to other highly connected vertices, and have broad degree distributions. We present a model for an undirected growing network which reproduces these characteristics, with the aim of producing efficiently very large networks to be used as platforms for studying sociodynamic phenomena. The communities arise from a mixture of random attachment and implicit preferential attachment. The structural properties of the model are studied analytically and numerically, using the $k$-clique method for quantifying the communities.
Multivariable adaptive control
Hannu T. Toivonen
Modeling, Identification and Control , 1984, DOI: 10.4173/mic.1984.1.2
Abstract: In recent years there has been an extensive interest in adaptive and self-tuning controllers, and there is a vast literature on various adaptive algorithms. The purpose of the present paper is to review some common approaches for multi-variable adaptive control. The presentation concentrates on procedures which are based on stochastic controller design methods, but some close connections with other design techniques are also indicated.
Multivariable controller for discrete stochastic amplitude-constrained systems
Hannu T. Toivonen
Modeling, Identification and Control , 1983, DOI: 10.4173/mic.1983.2.2
Abstract: A sub-optimal multivariable controller for discrete stochastic amplitude-constrained systems is presented. In the approach the regulator structure is restricted to the class of linear saturated feedback laws. The stationary covariances of the controlled system are evaluated by approximating the stationary probability distribution of the state by a gaussian distribution. An algorithm for minimizing a quadratic loss function is given, and examples are presented to illustrate the performance of the sub-optimal controller.
A survey of data mining methods for linkage disequilibrium mapping
P?ivi Onkamo, Hannu Toivonen
Human Genomics , 2006, DOI: 10.1186/1479-7364-2-5-336
Allocating Logging Rights in Peruvian Amazonia—Does It Matter to Be Local?
Matti Salo,Samuli Helle,Tuuli Toivonen
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0019704
Abstract: The fate of tropical forests is a global concern, yet many far-reaching decisions affecting forest resources are made locally. We explore allocation of logging rights using a case study from Loreto, Peruvian Amazonia, where millions of hectares of tropical rainforest were offered for concession in a competitive tendering process that addressed issues related to locality.
HaploRec: efficient and accurate large-scale reconstruction of haplotypes
Lauri Eronen, Floris Geerts, Hannu Toivonen
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-542
Abstract: We define three novel statistical models and give an efficient algorithm for haplotype reconstruction, jointly called HaploRec. HaploRec is based on exploiting local regularities conserved in haplotypes: it reconstructs haplotypes so that they have maximal local coherence. This approach – not assuming statistical dependence for remotely located markers – has two useful properties: it is well-suited for sparse marker maps, such as those used in gene mapping, and it can actually take advantage of long maps.Our experimental results with simulated and real data show that HaploRec is a powerful method for the large scale haplotyping needed in association studies. With sample sizes large enough for gene mapping it appeared to be the best compared to all other tested methods (Phase, fastPhase, PL-EM, Snphap, Gerbil; simulated data), with small samples it was competitive with the best available methods (real data). HaploRec is several orders of magnitude faster than Phase and comparable to the other methods; the running times are roughly linear in the number of subjects and the number of markers. HaploRec is publicly available at http://www.cs.helsinki.fi/group/genetics/haplotyping.html webcite.The problem we consider is haplotype reconstruction: given the genotypes of a sample of individuals, the task is to predict the most likely haplotype pair for each individual. Computational haplotype reconstruction methods are based on statistical dependency between closely located markers, known as linkage disequilibrium. Many computational methods have been developed for the reconstruction of haplotypes. Some of these methods do not rely on the statistical modeling of the haplotypes [1-3], but most of them, like our proposed algorithm HaploRec, do [4-10]. For a review of these and other haplotyping methods we refer to [11-13]. Laboratory techniques are being developed for direct molecular haplotyping (see, e.g., [14,15]), but these techniques are not mature yet, and are currently t
Biomine: predicting links between biological entities using network models of heterogeneous databases
Lauri MA Eronen, Hannu TT Toivonen
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-119
Abstract: Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes.The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi webcite, allowing searching
The Use of Weighted Graphs for Large-Scale Genome Analysis
Fang Zhou, Hannu Toivonen, Ross D. King
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0089618
Abstract: There is an acute need for better tools to extract knowledge from the growing flood of sequence data. For example, thousands of complete genomes have been sequenced, and their metabolic networks inferred. Such data should enable a better understanding of evolution. However, most existing network analysis methods are based on pair-wise comparisons, and these do not scale to thousands of genomes. Here we propose the use of weighted graphs as a data structure to enable large-scale phylogenetic analysis of networks. We have developed three types of weighted graph for enzymes: taxonomic (these summarize phylogenetic importance), isoenzymatic (these summarize enzymatic variety/redundancy), and sequence-similarity (these summarize sequence conservation); and we applied these types of weighted graph to survey prokaryotic metabolism. To demonstrate the utility of this approach we have compared and contrasted the large-scale evolution of metabolism in Archaea and Eubacteria. Our results provide evidence for limits to the contingency of evolution.
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