liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 28 of 28
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Austin, Brian
    et al.
    Deparrment of Biological Sciences Heriot-Watt University.
    Dawyndt, Peter
    Lab. of Microbiology University of Ghent.
    Gyllenberg, Mats
    Dept. of mathematics University of Turku.
    Lund, Tatu
    Nokia Mobile Phones.
    Swings, Jean
    Lab. of microbiology Univesrity of Ghent.
    Thompson, Fabiano
    Lab. of microbiology University of Ghent.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Sliding window discretization: A new method for multiple band matching of bacterial genotyping fingerprints2004In: Bulletin of Mathematical Biology, ISSN 0092-8240, E-ISSN 1522-9602, Vol. 66, no 6, p. 1575-1596Article in journal (Refereed)
    Abstract [en]

    Microbiologists have traditionally applied hierarchical clustering algorithms as their mathematical tool of choice to unravel the taxonomic relationships between micro-organisms. However, the interpretation of such hierarchical classifications suffers from being subjective, in that a variety of ad hoc choices must be made during their construction. On the other hand, the application of more profound and objective mathematical methods - such as the minimization of stochastic complexity - for the classification of bacterial genotyping fingerprints data is hampered by the prerequisite that such methods only act upon vectorized data. In this paper we introduce a new method, coined sliding window discretization, for the transformation of genotypic fingerprint patterns into binary vector format. In the context of an extensive amplified fragment length polymorphism (AFLP) data set of 507 strains from the Vibrionaceae family that has previously been analysed, we demonstrate by comparison with a number of other discretization methods that this new discretization method results in minimal loss of the original information content captured in the banding patterns. Finally, we investigate the implications of the different discretization methods on the classification of bacterial genotyping fingerprints by minimization of stochastic complexity, as it is implemented in the BinClass software package for probabilistic clustering of binary vectors. The new taxonomic insights learned from the resulting classification of the AFLP patterns will prove the value of combining sliding window discretization with minimization of stochastic complexity, as an alternative classification algorithm for bacterial genotyping fingerprints.

  • 2.
    Corander, Jukka
    et al.
    Department of Mathematics, Åbo Akademi University, Åbo, Finland.
    Ekdahl, Magnus
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    A bayesian random fragment insertion model for de novo detection of DNA regulatory binding regions2007Manuscript (preprint) (Other academic)
    Abstract [en]

    Identification of regulatory binding motifs within DNA sequences is a commonly occurring problem in computationnl bioinformatics. A wide variety of statistical approaches have been proposed in the literature to either scan for previously known motif types or to attempt de novo identification of a fixed number (typically one) of putative motifs. Most approaches assume the existence of reliable biodatabasc information to build probabilistic a priori description of the motif classes. No method has been previously proposed for finding the number of putative de novo motif types and their positions within a set of DNA sequences. As the number of sequenced genomes from a wide variety of organisms is constantly increasing, there is a clear need for such methods. Here we introduce a Bayesian unsupervised approach for this purpose by using recent advances in the theory of predictive classification and Markov chain Monte Carlo computation. Our modelling framework enables formal statistical inference in a large-scale sequence screening and we illustrate it by a set of examples.

  • 3.
    Corander, Jukka
    et al.
    Department of Mathematics, Åbo Akademi University, Åbo, Finland.
    Ekdahl, Magnus
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Department of Mathematics, Royal Institute of Technology, Stockholm, Sweden.
    Parallell interacting MCMC for learning of topologies of graphical models2008In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 17, no 3, p. 431-456Article in journal (Refereed)
    Abstract [en]

    Automated statistical learning of graphical models from data has attained a considerable degree of interest in the machine learning and related literature. Many authors have discussed and/or demonstrated the need for consistent stochastic search methods that would not be as prone to yield locally optimal model structures as simple greedy methods. However, at the same time most of the stochastic search methods are based on a standard Metropolis–Hastings theory that necessitates the use of relatively simple random proposals and prevents the utilization of intelligent and efficient search operators. Here we derive an algorithm for learning topologies of graphical models from samples of a finite set of discrete variables by utilizing and further enhancing a recently introduced theory for non-reversible parallel interacting Markov chain Monte Carlo-style computation. In particular, we illustrate how the non-reversible approach allows for novel type of creativity in the design of search operators. Also, the parallel aspect of our method illustrates well the advantages of the adaptive nature of search operators to avoid trapping states in the vicinity of locally optimal network topologies.

  • 4.
    Dawyndt, Peter
    et al.
    Laboratorium voor Microbiologie University of Gent.
    Swings, Jean
    Laboratorium voor microbiologie University of Gent.
    Austin, Brian
    School of Biological Sciences Heriot-Watt University.
    Thompson, Fabiano
    laboratorium voor microbiologie University of Gent.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Gyllenberg, Mats
    Department of mathematics and statistics University of Helsinki.
    A complementary approach to systematics2005In: Microbiology Today, ISSN 1464-0570, no February, p. 38-38Article in journal (Other academic)
  • 5.
    Dawyndt, Peter
    et al.
    Laboratorium voor Microbiologie University of Gent.
    Thompson, Fabiano
    Laboratorium voor Microbiologie University of Gent.
    Austin, Brian
    School of Life Sciences Heriot-Watt University.
    Swings, Jean
    laboratorium voor Microbiologie University of Gent.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Gyllenberg, Mats
    Department of Mathematics University of Turku.
    Application of sliding-window discretization and minimization of stochastic complexity for the analysis of fAFLP genotyping fingerprint patterns of Vibrionaceae2005In: International Journal of Systematic and Evolutionary Microbiology, ISSN 1466-5026, E-ISSN 1466-5034, Vol. 55, no 1, p. 57-66Article in journal (Refereed)
    Abstract [en]

    Minimization of stochastic complexity (SC) was used as a method for classification of genotypic fingerprints. The method was applied to fluorescent amplified fragment length polymorphism (fAFLP) fingerprint patterns of 507 Vibrionaceae representatives. As the current BinClass implementation of the optimization algorithm for classification only works on binary vectors, the original fingerprints were discretized in a preliminary step using the sliding-window band-matching method, in order to maximally preserve the information content of the original band patterns. The novel classification generated using the BinClass software package was subjected to an in-depth comparison with a hierarchical classification of the same dataset, in order to acknowledge the applicability of the new classification method as a more objective algorithm for the classification of genotyping fingerprint patterns. Recent DNA-DNA hybridization and 16S rRNA gene sequence experiments proved that the classification based on SC-minimization forms separate clusters that contain the fAFLP patterns for all representatives of the species Enterovibrio norvegicus, Vibrio fortis, Vibrio diazotrophicus or Vibrio campbellii, while previous hierarchical cluster analysis had suggested more heterogeneity within the fAFLP patterns by splitting the representatives of the above-mentioned species into multiple distant clusters. As a result, the new classification methodology has highlighted some previously unseen relationships within the biodiversity of the family Vibrionaceae.

  • 6.
    Ekdahl, Magnus
    et al.
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation2006In: Journal of Machine Learning Research, ISSN 1532-4435, Vol. 7, p. 2449-2480Article in journal (Refereed)
    Abstract [en]

    In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification. We present an analysis valid in general, and easily computable formulas for estimating the degradation in probability of correct classification when compared to the optimal classifier. An example of an approximation is the Na¨ıve Bayes classifier. We show that the performance of the Naïve Bayes depends on the degree of functional dependence between the features and labels. We provide a sufficient condition for zero loss of performance, too.

  • 7.
    Ekdahl, Magnus
    et al.
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers2007In: Machine Learning and Data Mining in Pattern Recognition: 5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007. Proceedings / [ed] Petra Perner, Springer Berlin/Heidelberg, 2007, p. 2-16Chapter in book (Refereed)
    Abstract [en]

    Computational procedures using independence assumptions in various forms are popular in machine learning, although checks on empirical data have given inconclusive results about their impact. Some theoretical understanding of when they work is available, but a definite answer seems to be lacking. This paper derives distributions that maximizes the statewise difference to the respective product of marginals. These distributions are, in a sense the worst distribution for predicting an outcome of the data generating mechanism by independence. We also restrict the scope of new theoretical results by showing explicitly that, depending on context, independent ('Naïve') classifiers can be as bad as tossing coins. Regardless of this, independence may beat the generating model in learning supervised classification and we explicitly provide one such scenario.

  • 8.
    Ekdahl, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    On the Performance of Approximations of Bayesian Networks in Model-2006In: The Annual Workshop of the Swedish Artificial Intelligence Society,2006, Umeå: SAIS , 2006, p. 73-Conference paper (Refereed)
    Abstract [en]

    When the true class conditional model and class probabilities are approximated in a pattern recognition/classification problem the performance of the optimal classifier is expected to deteriorate. But calculating this reduction is far from trivial in the general case. We present one generalization, and easily computable formulas for estimating the degradation in performance with respect to the optimal classifier. An example of an approximation is the Naive Bayes classifier. We generalize and sharpen results for evaluating this classifier.

  • 9.
    Ekdahl, Magnus
    et al.
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Ohlson, Martin
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Concentrated or non-concentrated discrete distributions are almost independent2007Manuscript (preprint) (Other academic)
    Abstract [en]

    The task of approximating a simultaneous distribution with a product of distributions in a single variable is important in the theory and applications of classification and learning, probabilistic reasoning, and random algmithms. The evaluation of the goodness of this approximation by statistical independence amounts to bounding uniformly upwards the difference between a joint distribution and the product of the distributions (marginals). In this paper we develop a bound that uses information about the most probable state to find a sharp estimate, which is often as sharp as possible. We also examine the extreme cases of concentration and non-conccntmtion, respectively, of the approximated distribution.

  • 10.
    Gyllenberg, M.
    et al.
    Department of Mathematics, University of Turku, 20014 Turku, Finland.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Bayesian predictiveness, exchangeability and sufficientness in bacterial taxonomy2002In: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 177-178, p. 161-184Conference paper (Other academic)
    Abstract [en]

    We present a theory of classification and predictive identification of bacteria. Bacterial strains are characterized by a binary vector and the taxonomy is specified by attaching a label to each vector. The theory is developed from only two basic assumptions, viz. that the sequence of pairs of feature vectors and the attached labels is judged (infinitely) exchangeable and predictively sufficient. We derive expressions for the training error and the probability of identification error and show that latter is an affine function of the former. We prove the law of large numbers for identification matrices, which contain the fundamental information of bacterial data. We prove the Bayesian risk consistency of the predictive identification rule given by the theory and show that the training error is a consistent estimate of the generalization error. © 2002 Published by Elsevier Science Inc.

  • 11. Gyllenberg, M
    et al.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Probabilistic models for bacterial taxonomy2001In: International Statistical Review, ISSN 0306-7734, E-ISSN 1751-5823, Vol. 69, no 2, p. 249-276Article, review/survey (Refereed)
    Abstract [en]

    We give a survey of different partitioning methods that have been applied to bacterial taxonomy. We introduce a theoretical framework, which makes it possible to treat the various models in a unified way. The key concepts of our approach are prediction and storing of microbiological information in a Bayesian forecasting setting. We show that there is a close connection between classification and probabilistic identification and that, in fact, our approach ties these two concepts together in a coherent way.

  • 12.
    Gyllenberg, M
    et al.
    Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Dawyndt, P
    Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
    Lund, T
    Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
    Thompson, F
    Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
    Austin, B
    Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
    Swings, J
    Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
    New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification2002In: Systematic and Applied Microbiology, ISSN 0723-2020, E-ISSN 1618-0984, Vol. 25, no 3, p. 403-415Article in journal (Refereed)
    Abstract [en]

    We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.

  • 13.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Bacterial identification using 16S rRNA fragments by hidden Markov models2003In: 54th Session of the International Statistical Institute,2003, 2003Conference paper (Other academic)
  • 14.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Dynamic Bayesian networks in a study of genetic interactions2003In: Föreningen för medicinsk statistik, årsmöte,2003, 2003Conference paper (Other academic)
  • 15.
    Koski, Timo
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Lectures at RNI on Probabilistic Models and Inference for Phylogenetics2004Report (Other academic)
    Abstract [en]

    The core of these lecture notes corresponds to the contents of a series of seminars held during November-December, 2003, at the Division of Biometry of the Rolf Nevanlinna Institute (RNI) (a research institute of mathematics, computer science and statistics, University of Helsinki). The seminars were organized within the “Centre of Population Genetic Analyses” at RNI. The centre is funded by a grant from the Academy of Finland.

    The author thanks Professor Elja Arjas, head of the Division of Biometry, for the invitation to give these lectures and for several comments that have improved both the contents and of the presentation.

    The original impetus for the studies underlying these notes was to understand certain issues in bacterial taxonomy (see e.g., Busse et.al. 1996, Gaunt et.al. 2001, Mougel et.al. 2002, Van de Peer et.al. 1993) especially when construction hidden Markov models using Hobohnm algorithm for preprocessing of sequence data.

    The benefit of these lectures for their audience might be to have been exposed to many of the concepts and models that are being mentioned in the section on methods of a biological research paper like, e.g., (Gaunt et.al. 2001), and applied there, or in documents like PAML Manual (Yang 2002), MrByes Manual (Huelsenbeck and Ronquist 2001), and others.

    The current version of the notes is not final; several additional sections and chapters are under construction. One obvious shortcoming of this version is that every item in the Bibliography is not necessarily referred to in the text. Some of the figures in the text have a sketchy standard.

  • 16.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Corander, Jukka
    Institutionen för matematik och statistik Helsingfors universitet.
    Gyllenberg, Mats
    Insitution för matematik och statistik Helsingfors universitet.
    Bayesian model learning based on a parallel MCMC strategy2006In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375, Vol. 16, no 2, p. 355-362Article in journal (Refereed)
    Abstract [en]

      Interacting parallell Markov chains are shown to converge to a maximum of the posterior on a set of partitions of a finite set of discrete data. This is demonstrated on an example of population genetics data.

  • 17.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Corander, Jukka
    Department of Mathematics and Statistics University of Helsinki.
    Gyllenberg, Mats
    Department of Mathematics and Statistics University of Helsinki.
    Random Partition Models and Exchangeability for Bayesian Identification of Population Structure2007In: Bulletin of Mathematical Biology, ISSN 0092-8240, E-ISSN 1522-9602, Vol. 69, no 3, p. 797-815Article in journal (Refereed)
    Abstract [en]

     We introduce a statistical model for learning the genetic structure of populations. A novel MCMC type method is introduced. 

  • 18.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Gyllenberg, M.
    Carlsson, J.
    Bayesian Network Classification of Binarized DNA Fingerprinting Patterns2003In: Mathematical modelling & computing in biology and medicine: 50th ESMTB Conference 2002 / [ed] Vincenzo Capasso, Bologna: Progetto Leonardo , 2003, p. 60-66Chapter in book (Other academic)
  • 19.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Hurd, H.
    The Wold isomorphism for cyclostationary sequences2004In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 84, no 5, p. 813-824Article in journal (Refereed)
    Abstract [en]

    In 1948 Wold introduced an isometric isomorphism between a Hilbert (linear) space formed from the weighted shifts of a numerical sequence and a suitable Hilbert space of values of a second-order stochastic sequence. Motivated by a recent resurrection of the idea in the context of cyclostationary sequences and processes, we present the details of the Wold isomorphism between cyclostationary stochastic sequences and cyclostationary numerical sequences. We show how Hilbert-space representations of cyclostationary sequences are interpreted in the case of numerical CS sequences.

  • 20.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Hurd, Harry
    Cyclostationary Arrays: Their Unitary Operators and Representations2004In: Stochastic Processes and Functional Analysis:: A Volume of Recent Advances in Honor of M.M. Rao / [ed] Alan C. Krinik and Randall J. Swift., New York: Marcel Dekker Inc. , 2004, 1, p. 171-194Chapter in book (Other academic)
    Abstract [en]

    This extraordinary compilation is an expansion of the recent American Mathematical Society Special Session celebrating M. M. Rao's distinguished career and includes most of the presented papers as well as ancillary contributions from session invitees. This book shows the effectiveness of abstract analysis for solving fundamental problems of stochastic theory, specifically the use of functional analytic methods for elucidating stochastic processes, as made manifest in M. M. Rao's prolific research achievements. Featuring a biography of M. M. Rao, a complete bibliography of his published works, and meditations from former students, the book includes contributions from over 30 notable researchers.

  • 21.
    Koski, Timo
    et al.
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Noble, John
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Bayesian Networks: An Introduction2009 (ed. 1)Book (Other academic)
    Abstract [en]

    Bayesian Networks: An Introductionprovides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout.

    Features include:

    • An introduction to Dirichlet Distribution, Exponential Families and their applications.
    • A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.
    • A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning.
    • All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.

    This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology.

    Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.

  • 22.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Orre, Roland
    Norén, Niklas
    Statistics of the information component updated2003Report (Other academic)
  • 23.
    Koski, Timo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Rantanen, Ville-Veikko
    Biochemistry and Pharmacy Åbo Akademi.
    Gyllenberg, Mats
    Mathematics and Statistics University of Helsinki.
    Johnson, Mark S.
    Biochemistry and Pharmacy Åbo Akademi.
    A dissimilarity matrix between protein atom classes based on Gaussian mixtures2002In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 18, no 9, p. 1257-1263Article in journal (Refereed)
    Abstract [en]

    Motivation: Previously, Rantanen et al. (2001, J. Mol. Biol, 313, 197-214) constructed a protein atom-ligand fragment interaction library embodying experimentally solved, high-resolution three-dimensional (3D) structural data from the Protein Data Bank (PDB). The spatial locations of protein atoms that surround ligand fragments were modeled with Gaussian mixture models, the parameters of which were estimated with the expectation-maximization (EM) algorithm. In the validation analysis of this library, there was strong indication that the protein atom classification, 24 classes, was too large and that a reduction in the classes would lead to improved predictions. Results: Here, a dissimilarity (distance) matrix that is suitable for comparison and fusion of 24 pre-defined protein atom classes has been derived. Jeffreys' distances between Gaussian mixture models are used as a basis to estimate dissimilarities between protein atom classes. The dissimilarity data are analyzed both with a hierarchical clustering method and independently by using multidimensional scaling analysis. The results provide additional insight into the relationships between different protein atom classes, giving us guidance on, for example, how to readjust protein atom classification and, thus, they will help us to improve protein-ligand interaction predictions.

  • 24.
    Ohlson, Martin
    et al.
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Royal Institute of Technology, Sweden.
    On the Distribution of Matrix Quadratic Forms2012In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 41, no 18, p. 3403-315Article in journal (Refereed)
    Abstract [en]

    A characterization of the distribution of the multivariate quadratic form given by XAX′, where X is a p×n normally distributed matrix and A is an n×n symmetric real matrix, is presented. We show that the distribution of the quadratic form is the same as the distribution of a weighted sum of noncentralWishart distributed matrices. This is applied to derive the distribution of the sample covariance between the rows of X when the expectation is the same for every column and is estimated with the regular mean.

  • 25.
    Ohlson, Martin
    et al.
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    Koski, Timo
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
    The Likelihood Ratio Statistic for Testing Spatial Independence using a Separable Covariance Matrix2009Report (Other academic)
    Abstract [en]

    This paper deals with the problem of testing spatial independence for dependent observations. The sample observationmatrix is assumed to follow a matrix normal distribution with a separable covariance matrix, in other words it can be written as a Kronecker product of two positive definite matrices. Two cases are considered, when the temporal covariance is known and when it is unknown. When the temporal covariance is known, the maximum likelihood estimates are computed and the asymptotic null distribution is given. In the case when the temporal covariance is unknown the maximum likelihood estimates of the parameters are found by an iterative alternating algori

  • 26.
    Rantanen, Ville-Veikko
    et al.
    Department of Biochemistry and Pharmacy Åbo Akademi University.
    Gyllenberg, Mats
    Department of Mathematics and statistics University of Helsinki.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Johnson, Mark S.
    Departmetn of Biochemistry and Pharmacy Åbo Akademi University.
    A Priori Contact Preferences in Molecular Recognition2005In: Journal of Bioinformatics and Computational Biology, ISSN 0219-7200, E-ISSN 1757-6334, Vol. 3, no 4, p. 861-890Article in journal (Refereed)
    Abstract [en]

    A molecular interaction library modeling favorable non-bonded interactions between atoms and molecular fragments is considered. In this paper, we represent the structure of the interaction library by a network diagram, which demonstrates that the underlying prediction model obtained for a molecular fragment is multi-layered. We clustered the molecular fragments into four groups by analyzing the pairwise distances between the molecular fragments. The distances are represented as an unrooted tree, in which the molecular fragments fall into four groups according to their function. For each fragment group, we modeled a group-specific a priori distribution with a Dirichlet distribution.

  • 27.
    Rantanen, V.-V.
    et al.
    Department of Mathematics, University of Turku, FIN-20014, Turku, Finland, Department of Biochemistry and Pharmacy, Åbo Akademi University, Tykistökatu 6 BioCity 3A, FIN-20521, Turku, Finland.
    Denessiouk, K.A.
    Department of Biochemistry and Pharmacy, Åbo Akademi University, Tykistökatu 6 BioCity 3A, FIN-20521, Turku, Finland.
    Gyllenberg, M.
    Department of Mathematics, University of Turku, FIN-20014, Turku, Finland.
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Johnson, M.S.
    Department of Biochemistry and Pharmacy, Åbo Akademi University, Tykistökatu 6 BioCity 3A, FIN-20521, Turku, Finland.
    A fragment library based on Gaussian mixtures predicting favorable molecular interactions2001In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 313, no 1, p. 197-214Article in journal (Refereed)
    Abstract [en]

    Here, a protein atom-ligand fragment interaction library is described. The library is based on experimentally solved structures of protein-ligand and protein-protein complexes deposited in the Protein Data Bank (PDB) and it is able to characterize binding sites given a ligand structure suitable for a protein. A set of 30 ligand fragment types were defined to include three or more atoms in order to unambiguously define a frame of reference for interactions of ligand atoms with their receptor proteins. Interactions between ligand fragments and 24 classes of protein target atoms plus a water oxygen atom were collected and segregated according to type. The spatial distributions of individual fragment - target atom pairs were visually inspected in order to obtain rough-grained constraints on the interaction volumes. Data fulfilling these constraints were given as input to an iterative expectation-maximization algorithm that produces as output maximum likelihood estimates of the parameters of the finite Gaussian mixture models. Concepts of statistical pattern recognition and the resulting mixture model densities are used (i) to predict the detailed interactions between Chlorella virus DNA ligase and the adenine ring of its ligand and (ii) to evaluate the "error" in prediction for both the training and validation sets of protein-ligand interaction found in the PDB. These analyses demonstrate that this approach can successfully narrow down the possibilities for both the interacting protein atom type and its location relative to a ligand fragment. © 2001 Academic Press.

  • 28. Rantanen, VV
    et al.
    Gyllenberg, M
    Koski, Timo
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
    Johnson, MS
    A Bayesian molecular interaction library2003In: Journal of Computer-Aided Molecular Design, ISSN 0920-654X, E-ISSN 1573-4951, Vol. 17, no 7, p. 435-461Article in journal (Refereed)
    Abstract [en]

    We describe a library of molecular fragments designed to model and predict non-bonded interactions between atoms. We apply the Bayesian approach, whereby prior knowledge and uncertainty of the mathematical model are incorporated into the estimated model and its parameters. The molecular interaction data are strengthened by narrowing the atom classification to 14 atom types, focusing on independent molecular contacts that lie within a short cutoff distance, and symmetrizing the interaction data for the molecular fragments. Furthermore, the location of atoms in contact with a molecular fragment are modeled by Gaussian mixture densities whose maximum a posteriori estimates are obtained by applying a version of the expectation-maximization algorithm that incorporates hyperparameters for the components of the Gaussian mixtures. A routine is introduced providing the hyperparameters and the initial values of the parameters of the Gaussian mixture densities. A model selection criterion, based on the concept of a 'minimum message length' is used to automatically select the optimal complexity of a mixture model and the most suitable orientation of a reference frame for a fragment in a coordinate system. The type of atom interacting with a molecular fragment is predicted by values of the posterior probability function and the accuracy of these predictions is evaluated by comparing the predicted atom type with the actual atom type seen in crystal structures. The fact that an atom will simultaneously interact with several molecular fragments forming a cohesive network of interactions is exploited by introducing two strategies that combine the predictions of atom types given by multiple fragments. The accuracy of these combined predictions is compared with those based on an individual fragment. Exhaustive validation analyses and qualitative examples ( e. g., the ligand-binding domain of glutamate receptors) demonstrate that these improvements lead to effective modeling and prediction of molecular interactions.

1 - 28 of 28
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf