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  • 1.
    Björnsson, Bergthor
    et al.
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology.
    Borrebaeck, Carl
    Lund Univ, Sweden.
    Elander, Nils
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology.
    Gasslander, Thomas
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology.
    Gawel, Danuta
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Jornsten, Rebecka
    Univ Gothenburg, Sweden; Chalmers Univ Technol, Sweden.
    Jung Lee, Eun Jung
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Yonsei Univ, South Korea.
    Li, Xinxiu
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Lilja, Sandra
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Martinez, David
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Matussek, Andreas
    Karolinska Univ Hosp, Sweden; Dept Lab Med, Sweden.
    Sandström, Per
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology.
    Schäfer, Samuel
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Stenmarker, Margaretha
    Futurum Acad Hlth and Care, Sweden; Inst Clin Sci, Sweden.
    Sun, Xiao-Feng
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Zhang, Huan
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Benson, Mikael
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Digital twins to personalize medicine2020In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 12, no 1, article id 4Article in journal (Other academic)
    Abstract [en]

    Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

    Download full text (pdf)
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  • 2.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Hussian, Mohamed
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Hasse diagrams and the generalized PAV-algorithm for monotonic regression in several explanatory variables2005In: Computational Statistics and Data Analysis, ISSN 0167-9473Article in journal (Refereed)
    Abstract [en]

    Monotonic regression is a nonparametric method for estimation ofmodels in which the expected value of a response variable y increases ordecreases in all coordinates of a vector of explanatory variables x = (x1, …, xp).Here, we examine statistical and computational aspects of our recentlyproposed generalization of the pool-adjacent-violators (PAV) algorithm fromone to several explanatory variables. In particular, we show how the goodnessof-fit and accuracy of obtained solutions can be enhanced by presortingobserved data with respect to their level in a Hasse diagram of the partial orderof the observed x-vectors, and we also demonstrate how these calculations canbe carried out to save computer memory and computational time. Monte Carlosimulations illustrate how rapidly the mean square difference between fittedand expected response values tends to zero, and how quickly the mean squareresidual approaches the true variance of the random error, as the number of observations increases up to 104.

  • 3.
    Burdakov, Oleg
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
    Sysoev, Oleg
    Linköping University, Department of Mathematics.
    Data preordering in generalized pav algorithm for monotonic regression2006Report (Other academic)
  • 4.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Sysoev, Oleg
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Data preordering in generalized PAV algorithm for monotonic regression2006In: Journal of Computational Mathematics, ISSN 0254-9409, E-ISSN 1991-7139, Vol. 24, no 6, p. 771-790Article in journal (Refereed)
    Abstract [en]

    Monotonic regression (MR) is a least distance problem with monotonicity constraints induced by a partially ordered data set of observations. In our recent publication [In Ser. {\sl Nonconvex Optimization and Its Applications}, Springer-Verlag, (2006) {\bf 83}, pp. 25-33], the Pool-Adjacent-Violators algorithm (PAV) was generalized from completely to partially ordered data sets (posets). The new algorithm, called GPAV, is characterized by the very low computational complexity, which is of second order in the number of observations. It treats the observations in a consecutive order, and it can follow any arbitrarily chosen topological order of the poset of observations. The GPAV algorithm produces a sufficiently accurate solution to the MR problem, but the accuracy depends on the chosen topological order. Here we prove that there exists a topological order for which the resulted GPAV solution is optimal. Furthermore, we present results of extensive numerical experiments, from which we draw conclusions about the most and the least preferable topological orders.

  • 5.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Generalized PAV algorithm with block refinement for partially ordered monotonic regression2009In: Proceedings of the Workshop on Learning Monotone Models from Data / [ed] A. Feelders and R. Potharst, 2009, p. 23-37Conference paper (Other academic)
    Abstract [en]

    In this paper, the monotonic regression problem (MR) is considered. We have recentlygeneralized for MR the well-known Pool-Adjacent-Voilators algorithm(PAV) from the case of completely to partially ordered data sets. Thenew algorithm, called GPAV, combines both high accuracy and lowcomputational complexity which grows quadratically with the problemsize. The actual growth observed in practice is typically far lowerthan quadratic. The fitted values of the exact MR solution composeblocks of equal values. Its GPAV approximation has also a blockstructure. We present here a technique for refining blocks produced bythe GPAV algorithm to make the new blocks more close to those in theexact solution. This substantially improves the accuracy of the GPAVsolution and does not deteriorate its computational complexity. Thecomputational time for the new technique is approximately triple thetime of running the GPAV algorithm. Its efficiency is demonstrated byresults of our numerical experiments.

  • 6.
    Burdakov, Oleg
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, Statistics.
    Sysoev, Oleg
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, Statistics.
    New optimization algorithms for large-scale isotonic regression in L2-norm2007In: EUROPT-OMS Conference on Optimization,2007, University of Hradec Kralove, Czech Republic: Guadeamus , 2007, p. 44-44Conference paper (Other academic)
    Abstract [en]

    Isotonic regression problem (IR) has numerous important applications in statistics, operations research, biology, image and signal processing and other areas. IR in L2-norm is a minimization problem in which the objective function is the squared Euclidean distance from a given point to a convex set defined by monotonicity constraints of the form: i-th component of the decision vector is less or equal to its j-th component. Unfortunately, the conventional optimization methods are unable to solve IR problems originating from large data sets. The existing IR algorithms, such as the minimum lower sets algorithm by Brunk, the min-max algorithm by Lee, the network flow algorithm by Maxwell & Muchstadt and the IBCR algorithm by Block et al. are able to find exact solution to IR problem for at most a few thousands of variables. The IBCR algorithm, which proved to be the most efficient of them, is not robust enough. An alternative approach is related to solving IR problem approximately. Following this approach, Burdakov et al. developed an algorithm, called GPAV, whose block refinement extension, GPAVR, is able to solve IR problems with a very high accuracy in a far shorter time than the exact algorithms. Apart from this, GPAVR is a very robust algorithm, and it allows us to solve IR problems with over hundred thousands of variables. In this talk, we introduce new exact IR algorithms, which can be viewed as active set methods. They use the approximate solution produced by the GPAVR algorithm as a starting point. We present results of our numerical experiments demonstrating the high efficiency of the new algorithms, especially for very large-scale problems, and their robustness. They are able to solve the problems which all existing exact IR algorithms fail to solve.

  • 7.
    Burdakov, Oleg
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, Statistics.
    Sysoev, Oleg
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, Statistics.
    Kapyrin, Ivan
    Institute of Numerical Mathematics Russian Academy of Sciences, Moscow, Russia.
    Vassilevski, Yuri
    Institute of Numerical Mathematics Russian Academy of Sciences, Moscow, Russia.
    Monotonic data fitting and interpolation with application to postprocessing of FE solutions2007In: CERFACS 20th Anniversary Conference on High-performance Computing,2007, 2007, p. 11-12Conference paper (Other academic)
    Abstract [en]

    In this talk we consider the isotonic regression (IR) problem which can be formulated as follows. Given a vector $\bar{x} \in R^n$, find $x_* \in R^n$ which solves the problem: \begin{equation}\label{ir2} \begin{array}{cl} \mbox{min} & \|x-\bar{x}\|^2 \\ \mbox{s.t.} & Mx \ge 0. \end{array} \end{equation} The set of constraints $Mx \ge 0$ represents here the monotonicity relations of the form $x_i \le x_j$ for a given set of pairs of the components of $x$. The corresponding row of the matrix $M$ is composed mainly of zeros, but its $i$th and $j$th elements, which are equal to $-1$ and $+1$, respectively. The most challenging applications of (\ref{ir2}) are characterized by very large values of $n$. We introduce new IR algorithms. Our numerical experiments demonstrate the high efficiency of our algorithms, especially for very large-scale problems, and their robustness. They are able to solve some problems which all existing IR algorithms fail to solve. We outline also our new algorithms for monotonicity-preserving interpolation of scattered multivariate data. In this talk we focus on application of our IR algorithms in postprocessing of FE solutions. Non-monotonicity of the numerical solution is a typical drawback of the conventional methods of approximation, such as finite elements (FE), finite volumes, and mixed finite elements. The problem of monotonicity is particularly important in cases of highly anisotropic diffusion tensors or distorted unstructured meshes. For instance, in the nuclear waste transport simulation, the non-monotonicity results in the presence of negative concentrations which may lead to unacceptable concentration and chemistry calculations failure. Another drawback of the conventional methods is a possible violation of the discrete maximum principle, which establishes lower and upper bounds for the solution. We suggest here a least-change correction to the available FE solution $\bar{x} \in R^n$. This postprocessing procedure is aimed on recovering the monotonicity and some other important properties that may not be exhibited by $\bar{x}$. The mathematical formulation of the postprocessing problem is reduced to the following convex quadratic programming problem \begin{equation}\label{ls2} \begin{array}{cl} \mbox{min} & \|x-\bar{x}\|^2 \\ \mbox{s.t.} & Mx \ge 0, \quad l \le x \le u, \quad e^Tx = m, \end{array} \end{equation} where$e=(1,1, \ldots ,1)^T \in R^n$. The set of constraints $Mx \ge 0$ represents here the monotonicity relations between some of the adjacent mesh cells. The constraints $l \le x \le u$ originate from the discrete maximum principle. The last constraint formulates the conservativity requirement. The postprocessing based on (\ref{ls2}) is typically a large scale problem. We introduce here algorithms for solving this problem. They are based on the observation that, in the presence of the monotonicity constraints only, problem (\ref{ls2}) is the classical monotonic regression problem, which can be solved efficiently by some of the available monotonic regression algorithms. This solution is used then for producing the optimal solution to problem (\ref{ls2}) in the presence of all the constraints. We present results of numerical experiments to illustrate the efficiency of our algorithms.

  • 8.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    A Dual Active-Set Algorithm for Regularized Monotonic Regression2017In: Journal of Optimization Theory and Applications, ISSN 0022-3239, E-ISSN 1573-2878, Vol. 172, no 3, p. 929-949Article in journal (Refereed)
    Abstract [en]

    Monotonic (isotonic) regression is a powerful tool used for solving a wide range of important applied problems. One of its features, which poses a limitation on its use in some areas, is that it produces a piecewise constant fitted response. For smoothing the fitted response, we introduce a regularization term in the monotonic regression, formulated as a least distance problem with monotonicity constraints. The resulting smoothed monotonic regression is a convex quadratic optimization problem. We focus on the case, where the set of observations is completely (linearly) ordered. Our smoothed pool-adjacent-violators algorithm is designed for solving the regularized problem. It belongs to the class of dual active-set algorithms. We prove that it converges to the optimal solution in a finite number of iterations that does not exceed the problem size. One of its advantages is that the active set is progressively enlarging by including one or, typically, more constraints per iteration. This resulted in solving large-scale test problems in a few iterations, whereas the size of that problems was prohibitively too large for the conventional quadratic optimization solvers. Although the complexity of our algorithm grows quadratically with the problem size, we found its running time to grow almost linearly in our computational experiments.

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  • 9.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    A Dual Active-Set Algorithm for Regularized Slope-Constrained Monotonic Regression2017In: Iranian Journal of Operations Research, ISSN 2008-1189, Vol. 8, no 2, p. 40-47Article in journal (Refereed)
    Abstract [en]

    In many problems, it is necessary to take into account monotonic relations. Monotonic (isotonic) Regression (MR) is often involved in solving such problems. The MR solutions are of a step-shaped form with a typical sharp change of values between adjacent steps. This, in some applications, is regarded as a disadvantage. We recently introduced a Smoothed MR (SMR) problem which is obtained from the MR by adding a regularization penalty term. The SMR is aimed at smoothing the aforementioned sharp change. Moreover, its solution has a far less pronounced step-structure, if at all available. The purpose of this paper is to further improve the SMR solution by getting rid of such a structure. This is achieved by introducing a lowed bound on the slope in the SMR. We call it Smoothed Slope-Constrained MR (SSCMR) problem. It is shown here how to reduce it to the SMR which is a convex quadratic optimization problem. The Smoothed Pool Adjacent Violators (SPAV) algorithm developed in our recent publications for solving the SMR problem is adapted here to solving the SSCMR problem. This algorithm belongs to the class of dual active-set algorithms. Although the complexity of the SPAV algorithm is o(n2) its running time is growing in our computational experiments almost linearly with n. We present numerical results which illustrate the predictive performance quality of our approach. They also show that the SSCMR solution is free of the undesirable features of the MR and SMR solutions.

  • 10.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Regularized monotonic regression2016Report (Other academic)
    Abstract [en]

    Monotonic (isotonic) Regression (MR) is a powerful tool used for solving a wide range of important applied problems. One of its features, which poses a limitation on its use in some areas, is that it produces a piecewise constant fitted response. For smoothing the fitted response, we introduce a regularization term in the MR formulated as a least distance problem with monotonicity constraints. The resulting Smoothed Monotonic Regrassion (SMR) is a convex quadratic optimization problem. We focus on the SMR, where the set of observations is completely (linearly) ordered. Our Smoothed Pool-Adjacent-Violators (SPAV) algorithm is designed for solving the SMR. It belongs to the class of dual activeset algorithms. We proved its finite convergence to the optimal solution in, at most, n iterations, where n is the problem size. One of its advantages is that the active set is progressively enlarging by including one or, typically, more constraints per iteration. This resulted in solving large-scale SMR test problems in a few iterations, whereas the size of that problems was prohibitively too large for the conventional quadratic optimization solvers. Although the complexity of the SPAV algorithm is O(n2), its running time was growing in our computational experiments in proportion to n1:16.

    Download full text (pdf)
    Regularized Monotonic Regression
  • 11.
    Burdakov, Oleg
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics.
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
    Hussian, Mohamed
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
    An algorithm for isotonic regression problems2004In: European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS / [ed] P. Neittaanmäki, T. Rossi, K. Majava and O. Pironneau, Jyväskylä: University of Jyväskylä , 2004, p. 1-9Conference paper (Refereed)
    Abstract [en]

    We consider the problem of minimizing the distance from a given n-dimensional vector to a set defined by constraintsof the form   xi  xj Such constraints induce a partial order of the components xi, which can be illustrated by an acyclic directed graph.This problem is known as the isotonic regression (IR) problem. It has important applications in statistics, operations research and signal processing. The most of the applied IR problems are characterized by a very large value of n. For such large-scale problems, it is of great practical importance to develop algorithms whose complexity does not rise with n too rapidly.The existing optimization-based algorithms and statistical IR algorithms have either too high computational complexity or too low accuracy of the approximation to the optimal solution they generate. We introduce a new IR algorithm, which can be viewed as a generalization of the Pool-Adjacent-Violator (PAV) algorithm from completely to partially ordered data. Our algorithm combines both low computational complexity O(n2) and high accuracy. This allows us to obtain sufficiently accurate solutions to the IR problems with thousands of observations.

  • 12.
    Burdakov, Oleg
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Sysoev, Oleg
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
    Hussian, Mohamed
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
    An O(n2) algorithm for isotonic regression2006In: Large-Scale Nonlinear Optimization / [ed] Pillo, Gianni; Roma, Massimo, New York: Springer Science+Business Media B.V., 2006, p. 25-33Conference paper (Other academic)
    Abstract [en]

    We consider the problem of minimizing the distance from a given n-dimensional vector to a set defined by constraints of the form xixj. Such constraints induce a partial order of the components xi, which can be illustrated by an acyclic directed graph. This problem is also known as the isotonic regression (IR) problem. IR has important applications in statistics, operations research and signal processing, with most of them characterized by a very large value of n. For such large-scale problems, it is of great practical importance to develop algorithms whose complexity does not rise with n too rapidly. The existing optimization-based algorithms and statistical IR algorithms have either too high computational complexity or too low accuracy of the approximation to the optimal solution they generate. We introduce a new IR algorithm, which can be viewed as a generalization of the Pool-Adjacent-Violator (PAV) algorithm from completely to partially ordered data. Our algorithm combines both low computational complexity O(n2) and high accuracy. This allows us to obtain sufficiently accurate solutions to IR problems with thousands of observations.

  • 13.
    Burdakov, Oleg
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Hussian, Mohammed
    Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Arts and Sciences.
    An O(n2) algorithm for isotonic regression problems2006In: Large-Scale Nonlinear Optimization / [ed] G. Di Pillo and M. Roma, Springer-Verlag , 2006, p. 25-33Chapter in book (Refereed)
    Abstract [en]

    Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.

    The chapters of the book, authored by some of the most active and well-known researchers in nonlinear optimization, give an updated overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications

  • 14.
    Gawel, Danuta
    et al.
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Bojner Horwitz, Eva
    Klinisk neurovetenskap, Karolinska institutet, Stockholm, Sverige.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Jacobsson, Bo
    Avdelningen för obstetrik och gynekologi, Göteborgs universitet; kvinnokliniken, Sahlgrenska universitetssjukhuset, Göteborg, Sverige.
    Jönsson, Jan-Ingvar
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Molecular Medicine and Virology. Linköping University, Faculty of Medicine and Health Sciences.
    Melén, Erik
    Institutionen för klinisk forskning och utbildning, Södersjukhuset, Karolinska institutet; Sachsska barn- och ungdomssjukhuset, Stockholm, Sverige.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Benson, Mikael
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus. Karolinska institutet, Stockholm, Sverige.
    Stor potential när genomikdatakan implementeras i klinisk rutin: [Clinical translation of genomic medicine]2021In: Läkartidningen, ISSN 0023-7205, E-ISSN 1652-7518, Vol. 118Article, review/survey (Refereed)
    Abstract [en]

    Recent technical developments and early clinical examples support that precision medicine has potential to provide novel diagnostic and therapeutic solutions for patients with complex diseases, who are not responding to existing therapies. Those solutions will require integration of genomic data with routine clinical, imaging, sensor, biobank and registry data. Moreover, user-friendly tools for informed decision support for both patients and clinicians will be needed. While this will entail huge technical, ethical, societal and regulatory challenges, it may contribute to transforming and improving health care towards becoming predictive, preventive, personalised and participatory (4P-medicine).

  • 15.
    Hussian, Mohamed
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Monotonic regression for the detection of temporal trends in environmental quality data2005In: Match, ISSN 0340-6253, Vol. 54, no 3, p. 535-550Article in journal (Refereed)
  • 16.
    Jung Lee, Eun Jung
    et al.
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Yonsei Univ, South Korea.
    Gawel, Danuta
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Lilja, Sandra
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Li, Xinxiu
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Schäfer, Samuel
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Zhang, Huan
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Benson, Mikael
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus.
    Analysis of expression profiling data suggests explanation for difficulties in finding biomarkers for nasal polyps2020In: Rhinology, ISSN 0300-0729, E-ISSN 1996-8604, Vol. 58, no 4, p. 360-367Article in journal (Refereed)
    Abstract [en]

    Background: Identification of clinically useful biomarkers for Nasal Polyposis in chronic rhinosinusitis (CRSwNP) has proven difficult. We analyzed gene expression profiling data to find explanations for this. Methods:We analyzed mRNA expression profiling data, GSE36830, of six uncinate tissues from healthy controls and six NP from CRSwNP patients. We performed Ingenuity Pathway Analysis (IPA) of differentially expressed genes to identify pathways and predicted upstream regulators. Results: We identified 1,608 differentially expressed genes and 177 significant pathways, of which Th1 and Th2 activation pathway and leukocyte extravasation signaling were most significant. We identified 75 upstream regulators whose activity was predicted to be upregulated.These included regulators of known pathogenic and therapeutic relevance, like IL-4. However, only seven of the 75 regulators were actually differentially expressed in NP, namely CSF1, TYROBP, CCL2, CCL11, SELP, ADORA3, ICAM1. Interestingly, these did not include IL-4, and four of the seven were receptors. This suggested a potential explanation for the discrepancy between the predicted and observed expression levels of the regulators, namely that the receptors, and not their ligands, were upregulated. Indeed, we found that 10 receptors of key predicted upstream regulators were upregulated, including IL4R. Conclusion: Our findings indicate that the difficulties in finding specific biomarkers for CRSwNP depend on the complex underlying mechanisms, which include multiple pathways and regulators, each of which may be subdivided into multiple components such as ligands, soluble and membrane-bound receptors. This suggests that combinations of biomarkers may be needed for CRSwNP diagnostics.

  • 17.
    Kalish, Michael L.
    et al.
    Syracuse University, USA.
    Dunn, John C.
    University of Adelaide, Australia.
    Burdakov, Oleg P.
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    A statistical test of the equality of latent orders2016In: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880, Vol. 70, p. 1-11, article id YJMPS2051Article in journal (Refereed)
    Abstract [en]

    It is sometimes the case that a theory proposes that the population means on two variables should have the same rank order across a set of experimental conditions. This paper presents a test of this hypothesis. The test statistic is based on the coupled monotonic regression algorithm developed by the authors. The significance of the test statistic is determined by comparison to an empirical distribution specific to each case, obtained via non-parametric or semi-parametric bootstrap. We present an analysis of the power and Type I error control of the test based on numerical simulation. Partial order constraints placed on the variables may sometimes be theoretically justified. These constraints are easily incorporated into the computation of the test statistic and are shown to have substantial effects on power. The test can be applied to any form of data, as long as an appropriate statistical model can be specified.

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  • 18.
    Kallestal, C.
    et al.
    Uppsala Univ, Sweden; London Sch Hyg and Trop Med, England.
    Zelaya, E. Blandon
    Asociac Desarrollo Econ and Sostenible El Espino AP, Nicaragua; UNAN Leon, Nicaragua.
    Pena, R.
    Uppsala Univ, Sweden; Pan Amer Hlth Org, Honduras.
    Perez, W.
    Uppsala Univ, Sweden.
    Contreras, M.
    Uppsala Univ, Sweden.
    Persson, L. A.
    Uppsala Univ, Sweden; London Sch Hyg and Trop Med, England.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Selling, K. Ekholm
    Uppsala Univ, Sweden.
    Predicting poverty. Data mining approaches to the health and demographic surveillance system in Cuatro Santos, Nicaragua2019In: International Journal for Equity in Health, E-ISSN 1475-9276, INTERNATIONAL JOURNAL FOR EQUITY IN HEALTH, Vol. 18, no 1, article id 165Article in journal (Refereed)
    Abstract [en]

    Background

    In order to further identify the needed interventions for continued poverty reduction in our study area Cuatro Santos, northern Nicaragua, we aimed to elucidate what predicts poverty, measured by the Unsatisfied Basic Need index. This analysis was done by using decision tree methodology applied to the Cuatro Santos health and demographic surveillance databases.

    Methods

    Using variables derived from the health and demographic surveillance update 2014, transferring individual data to the household level we used the decision tree framework Conditional Inference trees to predict the outcome “poverty” defined as two to four unsatisfied basic needs using the Unsatisfied Basic Need Index. We further validated the trees by applying Conditional random forest analyses in order to assess and rank the importance of predictors about their ability to explain the variation of the outcome “poverty.” The majority of the Cuatro Santos households provided information and the included variables measured housing conditions, assets, and demographic experiences since the last update (5 yrs), earlier participation in interventions and food security during the last 4 weeks.

    Results

    Poverty was rare in households that have some assets and someone in the household that has a higher education than primary school. For these households participating in the intervention that installed piped water with water meter was most important, but also when excluding this variable, the resulting tree showed the same results. When assets were not taken into consideration, the importance of education was pronounced as a predictor for welfare. The results were further strengthened by the validation using Conditional random forest modeling showing the same variables being important as predicting the outcome in the CI tree analysis. As assets can be a result, rather than a predictor of more affluence our results in summary point specifically to the importance of education and participation in the water installation intervention as predictors for more affluence.

    Conclusion

    Predictors of poverty are useful for directing interventions and in the Cuatro Santos area education seems most important to prioritize. Hopefully, the lessons learned can continue to develop the Cuatro Santos communities as well as development in similar poor rural settings around the world.

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  • 19.
    Kallestal, Carina
    et al.
    Uppsala Univ, Sweden.
    Blandon, Elmer Zelaya
    Asociac Desarrollo Econ and Sostenible El Espino AP, Nicaragua; Nicaraguan Autonomous Natl Univ Leon UNAN Leon, Nicaragua.
    Pena, Rodolfo
    Uppsala Univ, Sweden; Pan Amer Hlth Org, Honduras.
    Perez, Wilton
    Uppsala Univ, Sweden.
    Contreras, Mariela
    Uppsala Univ, Sweden.
    Persson, Lars-Ake
    Uppsala Univ, Sweden; London Sch Hyg and Trop Med, England.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Selling, Katarina Ekholm
    Uppsala Univ, Sweden.
    Assessing the Multiple Dimensions of Poverty. Data Mining Approaches to the 2004-14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua2020In: Frontiers In Public Health, ISSN 2296-2565, FRONTIERS IN PUBLIC HEALTH, Vol. 7, article id 409Article in journal (Refereed)
    Abstract [en]

    We identified clusters of multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance database, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems, prioritizing suggested actions, was initiated in 1997 and generated a community action plan 2002-2015. Interventions were school breakfasts, environmental protection, water and sanitation, preventive healthcare, home gardening, microcredit, technical training, university education stipends, and use of the Internet. In 2004, a survey of basic health and demographic information was performed in the whole population, followed by surveillance updates in 2007, 2009, and 2014 linking households and individuals. Information included the house material (floor, walls) and services (water, sanitation, electricity) as well as demographic data (birth, deaths, migration). Data on participation in interventions, food security, household assets, and womens self-rated health were collected in 2014. A K-means algorithm was used to cluster the household data (56 variables) in six clusters. The poverty ranking of household clusters using the unsatisfied basic needs index variables changed when including variables describing basic capabilities. The households in the fairly rich cluster with assets such as motorbikes and computers were described as modern. Those in the fairly poor cluster, having different degrees of food insecurity, were labeled vulnerable. Poor and poorest clusters of households were traditional, e.g., in using horses for transport. Results displayed a society transforming from traditional to modern, where the forerunners were not the richest but educated, had more working members in household, had fewer children, and were food secure. Those lagging were the poor, traditional, and food insecure. The approach may be useful for an improved understanding of poverty and to direct local policy and interventions.

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  • 20.
    Li, Xinxiu
    et al.
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Jung Lee, Eun Jung
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Yonsei Univ, South Korea.
    Lilja, Sandra
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Loscalzo, Joseph
    Brigham & Womens Hosp, MA 02115 USA; Harvard Med Sch, MA 02115 USA; Harvard Med Sch, MA 02115 USA.
    Schäfer, Samuel
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Smelik, Martin
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Strobl, Maria Regina
    Med Univ Vienna, Austria.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Wang, Hui
    Xuzhou Med Univ, Peoples R China.
    Zhang, Huan
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Zhao, Yelin
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Bohle, Barbara
    Med Univ Vienna, Austria.
    Benson, Mikael
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus. Karolinska Inst, Sweden.
    A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets2022In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 14, no 1, article id 48Article in journal (Refereed)
    Abstract [en]

    Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

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  • 21.
    Lilja, Sandra
    et al.
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Mavatar Inc, Sweden.
    Li, Xinxiu
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Karolinska Inst, Sweden.
    Smelik, Martin
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Karolinska Inst, Sweden.
    Lee, Eun Jung
    Yonsei Univ, South Korea.
    Loscalzo, Joseph
    Brigham & Womens Hosp, MA 02115 USA; Harvard Med Sch, MA 02115 USA.
    Marthanda, Pratheek Bellur
    Icahn Sch Med Mt Sinai, NY 10029 USA.
    Hu, Lang
    Xuzhou Med Univ, Peoples R China.
    Magnusson, Mattias
    Natl Board Hlth & Welf, Sweden.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Zhang, Huan
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Zhao, Yelin
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Karolinska Inst, Sweden.
    Sjöwall, Christopher
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Medicine Center, Department of Rheumatology.
    Gawel, Danuta
    Mavatar Inc, Sweden.
    Wang, Hui
    Xuzhou Med Univ, Peoples R China.
    Benson, Mikael
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus. Karolinska Inst, Sweden.
    Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases2023In: Cell Reports Medicine, E-ISSN 2666-3791 , Vol. 4, no 3, article id 100956Article in journal (Refereed)
    Abstract [en]

    Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory dis-eases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single -cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted mo-lecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro-and anti-inflammatory upstream regulators (URs) and downstream path-ways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

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  • 22.
    Perez, Wilton
    et al.
    Uppsala Univ, Sweden; Inst Nutr Cent Amer & Panama INCAP, Guatemala.
    Selling, Katarina Ekholm
    Uppsala Univ, Sweden.
    Blandon, Elmer Zelaya
    Assoc Desarrollo Econ & Sostenible Espino APRODES, Nicaragua; UNAN Leon, Nicaragua.
    Pena, Rodolfo
    Uppsala Univ, Sweden; Pan Amer Hlth Org, Honduras.
    Contreras, Mariela
    Uppsala Univ, Sweden.
    Persson, Lars-Åke
    Uppsala Univ, Sweden; London Sch Hyg & Trop Med, England.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Källestål, Carina
    Orebro Univ, Sweden.
    Trends and factors related to adolescent pregnancies: an incidence trend and conditional inference trees analysis of northern Nicaragua demographic surveillance data2021In: BMC Pregnancy and Childbirth, ISSN 1471-2393, E-ISSN 1471-2393, Vol. 21, no 1, article id 749Article in journal (Refereed)
    Abstract [en]

    Background We aimed to identify the 2001-2013 incidence trend, and characteristics associated with adolescent pregnancies reported by 20-24-year-old women. Methods A retrospective analysis of the Cuatro Santos Northern Nicaragua Health and Demographic Surveillance 2004-2014 data on women aged 15-19 and 20-24. To calculate adolescent birth and pregnancy rates, we used the first live birth at ages 10-14 and 15-19 years reported by women aged 15-19 and 20-24 years, respectively, along with estimates of annual incidence rates reported by women aged 20-24 years. We conducted conditional inference tree analyses using 52 variables to identify characteristics associated with adolescent pregnancies. Results The number of first live births reported by women aged 20-24 years was 361 during the study period. Adolescent pregnancies and live births decreased from 2004 to 2009 and thereafter increased up to 2014. The adolescent pregnancy incidence (persons-years) trend dropped from 2001 (75.1 per 1000) to 2007 (27.2 per 1000), followed by a steep upward trend from 2007 to 2008 (19.1 per 1000) that increased in 2013 (26.5 per 1000). Associated factors with adolescent pregnancy were living in low-education households, where most adults in the household were working, and high proportion of adolescent pregnancies in the local community. Wealth was not linked to teenage pregnancies. Conclusions Interventions to prevent adolescent pregnancy are imperative and must bear into account the context that influences the culture of early motherhood and lead to socioeconomic and health gains in resource-poor settings.

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  • 23.
    Svahn, Caroline
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Ericsson Res, Sweden.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    CCVAE: A Variational Autoencoder for Handling Censored Covariates2022In: 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, IEEE COMPUTER SOC , 2022, p. 709-714Conference paper (Refereed)
    Abstract [en]

    For time or safety critical scenarios when faulty predictions or decisions can have crucial consequences, such as in certain telecommunications scenarios, reliable prediction models and accurate data are of the essence. When modeling and predicting data in such scenarios, data with censored covariates remain an issue as ignoring them or imputing them with lack of precision may cause inaccurate or uncertain predictions. In this paper, we provide a fast, reliable Variational Autoencoder framework which can handle covariate censoring in high dimensional data. Our numerical experiments demonstrate that our framework compares favorably to alternative methods in terms of prediction accuracy for both the response and the covariates, while enabling estimation of the prediction uncertainties. We moreover demonstrate that the method is at least 8 times faster than the benchmark models used in this paper, and more robust to initial imputations and noise than existing models. The method can also be used directly for predicting unseen data, which is a challenge for some state-of-the-art methods.

  • 24.
    Svahn, Caroline
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Selective Imputation of Covariates in High Dimensional Censored Data2022In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715, Vol. 31, no 4, p. 1397-1405Article in journal (Refereed)
    Abstract [en]

    Efficient modeling of censored data, that is, data which are restricted by some detection limit or truncation, is important for many applications. Ignoring the censoring can be problematic as valuable information may be missing and restoration of these censored values may significantly improve the quality of models. There are many scenarios where one may encounter censored data: survival data, interval-censored data or data with a lower limit of detection. Strategies to handle censored data are plenty, however, little effort has been made to handle censored data of high dimension. In this article, we present a selective multiple imputation approach for predictive modeling when a larger number of covariates are subject to censoring. Our method allows for iterative, subject-wise selection of covariates to impute in order to achieve a fast and accurate predictive model. The algorithm furthermore selects values for imputation which are likely to provide important information if imputed. In contrast to previously proposed methods, our approach is fully nonparametric and therefore, very flexible. We demonstrate that, in comparison to previous work, our model achieves faster execution and often comparable accuracy in a simulated example as well as predicting signal strength in radio network data. for this article are available online.

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  • 25.
    Svahn, Caroline
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Ericsson AB, Sweden.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Cirkic, Mirsad
    Ericsson AB, Sweden.
    Gustafsson, Fredrik
    Ericsson AB, Sweden.
    Berglund, Joel
    Ericsson AB, Sweden.
    Inter-Frequency Radio Signal Quality Prediction for Handover, Evaluated in 3GPP LTE2019Conference paper (Refereed)
    Abstract [en]

    Radio resource management in cellular networks is typically based on device measurements reported to the serving base station. Frequent measuring of signal quality on available frequencies would allow for highly reliable networks and optimal connection at

  • 26.
    Svefors, Pernilla
    et al.
    Uppsala Universitet, Uppsala, Sweden; Center for Epidemiology and Community Medicine, Stockholm, Sweden.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Ekstrom, Eva-Charlotte
    Uppsala Universitet, Uppsala, Sweden.
    Persson, Lars Ake
    London School of Hygiene and Tropical Medicine, London, UK.
    Arifeen, Shams E
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
    Naved, Ruchira T
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
    Rahman, Anisur
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
    Khan, Ashraful Islam
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
    Selling, Katarina
    Uppsala Universitet, Uppsala, Sweden.
    Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh2019In: BMJ Open, E-ISSN 2044-6055, Vol. 9, no 8Article in journal (Refereed)
    Abstract [en]

    Introduction WHO has set a goal to reduce the prevalence of stunted child growth by 40% by the year 2025. To reach this goal, it is imperative to establish the relative importance of risk factors for stunting to deliver appropriate interventions. Currently, most interventions take place in late infancy and early childhood. This study aimed to identify the most critical prenatal and postnatal determinants of linear growth 0–24 months and the risk factors for stunting at 2 years, and to identify subgroups with different growth trajectories and levels of stunting at 2 years.

    Methods Conditional inference tree-based methods were applied to the extensive Maternal and Infant Nutrition Interventions in Matlab trial database with 309 variables of 2723 children, their parents and living conditions, including socioeconomic, nutritional and other biological characteristics of the parents; maternal exposure to violence; household food security; breast and complementary feeding; and measurements of morbidity of the mothers during pregnancy and repeatedly of their children up to 24 months of age. Child anthropometry was measured monthly from birth to 12 months, thereafter quarterly to 24 months.

    Results Birth length and weight were the most critical factors for linear growth 0–24 months and stunting at 2 years, followed by maternal anthropometry and parental education. Conditions after birth, such as feeding practices and morbidity, were less strongly associated with linear growth trajectories and stunting at 2 years.

    Conclusion The results of this study emphasise the benefit of interventions before conception and during pregnancy to reach a substantial reduction in stunting.

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  • 27.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Monotonic regression for large multivariate datasets2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Monotonic regression is a non-parametric statistical method that is designed especially for applications in which the expected value of a response variable increases or decreases in one or more explanatory variables. Such applications can be found in business, physics, biology, medicine, signal processing, and other areas. Inasmuch as many of the collected datasets can contain a very large number of multivariate observations, there is a strong need for efficient numerical algorithms. Here, we present new methods that make it feasible to fit monotonic functions to more than one hundred thousand data points. By simulation, we show that our algorithms have high accuracy and represent  considerable improvements with respect to computational time and memory requirements. In particular , we demonstrate how segmentation of a large-scale problem can greatly improve the performance of existing algorithms. Moreover, we show how the uncertainty of a monotonic regression model can be estimated. One of the procedures we developed can be employed to estimate the variance of the random error present in the observed response. Other procedures are based on resampling  techniques and can provide confidence intervals for the expected response at given levels of a set of predictors.

  • 28.
    Sysoev, Oleg
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Bartoszek, Krzysztof
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Ekström, Eva-Charlotte
    Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
    Ekström Selling, Katarina
    Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
    PSICA: Decision trees for probabilistic subgroup identification with categorical treatments2019In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 22, p. 4436-4452Article in journal (Refereed)
    Abstract [en]

    Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two‐arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community‐based nutrition intervention trial that justifies the validity of our method.

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  • 29.
    Sysoev, Oleg
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    A Smoothed Monotonic Regression via L2 Regularization2016Report (Other academic)
    Abstract [en]

    Monotonic Regression (MR) is a standard method for extracting a monotone function from non-monotonic data, and it is used in many applications. However, a known drawback of this method is that its fitted response is a piecewise constant function, while practical response functions are often required to be continuous. The method proposed in this paper achieves monotonicity and smoothness of the regression by introducing an L2 regularization term, and it is shown that the complexity of this method is O(n2). In addition, our simulations demonstrate that the proposed method normally has higher predictive power than some commonly used alternative methods, such as monotonic kernel smoothers. In contrast to these methods, our approach is probabilistically motivated and has connections to Bayesian modeling.

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    Smoothed Monotonic Regression via L2 Regularization
  • 30.
    Sysoev, Oleg
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    A smoothed monotonic regression via L2 regularization2019In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 59, no 1, p. 197-218Article in journal (Refereed)
    Abstract [en]

    Monotonic regression is a standard method for extracting a monotone function from non-monotonic data, and it is used in many applications. However, a known drawback of this method is that its fitted response is a piecewise constant function, while practical response functions are often required to be continuous. The method proposed in this paper achieves monotonicity and smoothness of the regression by introducing an L2 regularization term. In order to achieve a low computational complexity and at the same time to provide a high predictive power of the method, we introduce a probabilistically motivated approach for selecting the regularization parameters. In addition, we present a technique for correcting inconsistencies on the boundary. We show that the complexity of the proposed method is O(n2). Our simulations demonstrate that when the data are large and the expected response is a complicated function (which is typical in machine learning applications) or when there is a change point in the response, the proposed method has a higher predictive power than many of the existing methods.

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  • 31.
    Sysoev, Oleg
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    A segmentation-based algorithm for large-scale partially ordered monotonic regression2011In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 55, no 8, p. 2463-2476Article in journal (Refereed)
    Abstract [en]

    Monotonic regression (MR) is an efficient tool for estimating functions that are monotonic with respect to input variables. A fast and highly accurate approximate algorithm called the GPAV was recently developed for efficient solving large-scale multivariate MR problems. When such problems are too large, the GPAV becomes too demanding in terms of computational time and memory. An approach, that extends the application area of the GPAV to encompass much larger MR problems, is presented. It is based on segmentation of a large-scale MR problem into a set of moderate-scale MR problems, each solved by the GPAV. The major contribution is the development of a computationally efficient strategy that produces a monotonic response using the local solutions. A theoretically motivated trend-following technique is introduced to ensure higher accuracy of the solution. The presented results of extensive simulations on very large data sets demonstrate the high efficiency of the new algorithm.

  • 32.
    Sysoev, Oleg
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, Statistics.
    Burdakov, Oleg
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, Statistics.
    New optimization methods for isotonic regression in L1 norm2007In: EUROPT-OMS Conference on Optimization,2007, University of Hradec Kralove, Czech Republic: Guadeamus , 2007, p. 133-133Conference paper (Other academic)
    Abstract [en]

    Isotonic regression problem (IR) has numerous important applications in statistics, operations research, biology, image and signal processing and other areas. IR is a minimization problem with the objective function defined by the distance from a given point to a convex set defined by monotonicity constraints of the form: i-th component of the decision vector is less or equal to its j-th component. The distance in IR is usually associated with the Lp norm, whereas the norms L1 and L2 are of the highest practical interest. The conventional optimization methods are unable to solve large-scale IR problems originating from large data sets. Historically, the major efforts were focused on IR problem in the L2 norm. Exact algorithms such as the minimum lower sets algorithm by Brunk, the min-max algorithm by Lee, the network flow algorithm by Maxwell & Muchstadt and the IBCR algorithm by Block et al. were developed. Among them the IBCR algorithm has been proved to be the most numerically efficient, but it is not robust enough. An alternative approach is related to solving IR problem approximately. Following this approach, Burdakov et al. developed GPAV algorithm whose block refinement extension, GPAVR, is able to solve IR problem with high accuracy in a far shorter time than the exact algorithms. Apart from this, GPAVR is a very robust algorithm. Unfortunately, for the norm L1 there are no algorithms which are as efficient as those in L2 norm. In our talk, we introduce new algorithms, GPAVR1 and IBCR1. They are extensions of the algorithms GPAV and IBCR to L1 norm. We present also results of numerical experiments, which demonstrate the high efficiency of the new algorithms, especially for very large-scale problems.

  • 33.
    Sysoev, Oleg
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.
    Bootstrap confidence intervals for large-scale multivariate monotonic regression problems2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 3, p. 1025-1040Article in journal (Refereed)
    Abstract [en]

    Recently, the methods used to estimate monotonic regression (MR) models have been substantially improved, and some algorithms can now produce high-accuracy monotonic fits to multivariate datasets containing over a million observations. Nevertheless, the computational burden can be prohibitively large for resampling techniques in which numerous datasets are processed independently of each other. Here, we present efficient algorithms for estimation of confidence limits in large-scale settings that take into account the similarity of the bootstrap or jackknifed datasets to which MR models are fitted. In addition, we introduce modifications that substantially improve the accuracy of MR solutions for binary response variables. The performance of our algorithms isillustrated using data on death in coronary heart disease for a large population. This example also illustrates that MR can be a valuable complement to logistic regression.

  • 34.
    Sysoev, Oleg
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, Statistics.
    Grimvall, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, Statistics.
    Burdakov, Oleg
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
    Bootstrap estimation of the variance of the error term in monotonic regression models2013In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, no 4, p. 625-638Article in journal (Refereed)
    Abstract [en]

    The variance of the error term in ordinary regression models and linear smoothers is usually estimated by adjusting the average squared residual for the trace of the smoothing matrix (the degrees of freedom of the predicted response). However, other types of variance estimators are needed when using monotonic regression (MR) models, which are particularly suitable for estimating response functions with pronounced thresholds. Here, we propose a simple bootstrap estimator to compensate for the over-fitting that occurs when MR models are estimated from empirical data. Furthermore, we show that, in the case of one or two predictors, the performance of this estimator can be enhanced by introducing adjustment factors that take into account the slope of the response function and characteristics of the distribution of the explanatory variables. Extensive simulations show that our estimators perform satisfactorily for a great variety of monotonic functions and error distributions.

1 - 34 of 34
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