Search Results - "KÄRKKÄINEN, I"

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    Structural evaluation of the agonistic action of a vitamin D analog with two side chains binding to the nuclear vitamin D receptor by Väisänen, Sami, Peräkylä, Mikael, Kärkkäinen, Jouni I, Uskokovic, Milan R, Carlberg, Carsten

    Published in Molecular pharmacology (01-06-2003)
    “…The vitamin D receptor (VDR) is one of the endocrine members of the nuclear receptor superfamily and has a characteristic high affinity for its natural ligand…”
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    Journal Article
  3. 3

    Metalloprotease-Disintegrin (ADAM) Genes Are Widely and Differentially Expressed in the Adult CNS by Kärkkäinen, Iivari, Rybnikova, Elena, Pelto-Huikko, Markku, Huovila, Ari-Pekka J.

    Published in Molecular and cellular neuroscience (01-06-2000)
    “…ADAM family of metalloprotease-disintegrins, including enzymes that process TNF-α and β-amyloid precursor protein, has been indicated in neuronal development,…”
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    Journal Article
  4. 4

    ADAM9, ADAM10, and ADAM15 mRNA levels in the rat brain after kainic acid-induced status epilepticus by Ortiz, R.M., Kärkkäinen, I., Huovila, A.-P.J., Honkaniemi, J.

    Published in Brain research. Molecular brain research. (13-06-2005)
    “…ADAM metalloprotease-disintegrins mediate cell adhesion, proteolytic processing, and signal transduction. In the present study, the mRNA levels of ADAM9,…”
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    Journal Article
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    Developmental regulation and neuronal expression of the cellular disintegrin ADAM11 gene in mouse nervous system by Rybnikova, E, Kärkkäinen, I, Pelto-Huikko, M, Huovila, A.-P.J

    Published in Neuroscience (01-01-2002)
    “…ADAM11 is the prototype member of the predominantly CNS-associated clade of the ADAM metalloprotease-disintegrins that has been implicated in neural adhesion…”
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    Journal Article
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    Outlier detection using k-nearest neighbour graph by Hautamaki, V., Karkkainen, I., Franti, P.

    “…We present an outlier detection using indegree number (ODIN) algorithm that utilizes k-nearest neighbour graph. Improvements to existing kNN distance-based…”
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    Conference Proceeding
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    Maximum a Posteriori Adaptation of the Centroid Model for Speaker Verification by Hautamaki, Ville, Karkkainen, Tomi, Krkkinen, Ismo, Saastamoinen, Juhani, Tuononen, Marko, Franti, Pasi

    Published in IEEE signal processing letters (2008)
    “…Maximum a posteriori adapted Gaussian mixture model (GMM-MAP) is widely used in speaker verification. GMMs have three sets of parameters to be adapted: means,…”
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    Journal Article
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    Random swap EM algorithm for finite mixture models in image segmentation by Qinpei Zhao, Hautamaki, V., Karkkainen, I., Franti, P.

    “…The expectation-maximization (EM) algorithm is a popular tool in estimating model parameters, especially mixture models. As the EM algorithm is a hill-climbing…”
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    Conference Proceeding
  12. 12

    Critical role of helix 12 of the vitamin D(3) receptor for the partial agonism of carboxylic ester antagonists by Väisänen, Sami, Peräkylä, Mikael, Kärkkäinen, Jouni I, Steinmeyer, Andreas, Carlberg, Carsten

    Published in Journal of molecular biology (11-01-2002)
    “…The carboxy-terminal alpha-helix of a nuclear receptor ligand-binding domain (LBD), helix 12, contains a critical, ligand-modulated interface for the…”
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    Journal Article
  13. 13

    Dynamic local search for clustering with unknown number of clusters by Karkkainen, I., Franti, P.

    “…Dynamic clustering problems can be solved by finding several clustering solutions with different number of clusters, and by choosing the one that minimizes a…”
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    Conference Proceeding
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    Variable metric for binary vector quantization by Karkkainen I, Franti, P.

    “…We present a new method for performing vector quantization of binary vectors. The proposed method varies the distance metric and updates the centroids in an…”
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    Conference Proceeding
  16. 16

    Harmonic torque suppression by manual voltage injection by Karkkainen, I, Arkkio, A

    “…A method of suppressing the harmonic torques of electrical machines is presented. Three different saturated motors are simulated using finite element analysis…”
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    Conference Proceeding
  17. 17

    Gradual model generator for single-pass clustering by Karkkainen, I., Franti, P.

    “…We present an algorithm for generating a mixture model from data set by performing a single pass over the data. The method is applicable when the entire data…”
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    Conference Proceeding