ЗАСТОСУВАННЯ СТРУКТУРНО-ГРАФІЧНОГО ТА ГІС МОДЕЛЮВАННЯ У КАРТОГРАФІЧНИХ ДОСЛІДЖЕННЯХ

The inference and modeling of network‐like structures in genomic data is of prime importance in systems biology. Complex stochastic associations and interdependencies can very generally be described as a graphical model. However, the paucity of available samples in current high‐throughput experiments renders learning graphical models from genome data, such as microarray expression profiles, a challenging and very hard problem. Here we review several recently developed approaches to small‐sample inference of graphical Gaussian modeling and discuss strategies to cope with the high dimensionality of functional genomics data. Particular emphasis is put on regularization methods and an empirical Bayes network inference procedure.

BackgroundGraphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Bühlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models.MethodsWe analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index.We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer.ResultsBootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms.ConclusionsBootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes.

Graphical models facilitate the representation of psychological variables as complex systems of interacting variables structured as a network. However, their current statistical estimation procedures overlook the assumption of clustering, which refers to the grouping of subsets of variables that are more densely connected within the network, despite this assumption playing a central role in many psychological theories. We address this gap by proposing the use of the Stochastic Block Model (SBM) as a prior distribution on the network structure of a graphical model for binary and ordinal data. The SBM assumes that variables belong to latent clusters, where the probability of an edge depends on the cluster membership of the nodes. Embedding this prior in a Bayesian graphical modeling framework allows researchers to formally incorporate theoretical expectations about clustering, test hypotheses about the number of clusters, and estimate cluster assignments from cross-sectional data. We demonstrate the benefits of this approach in a simulation study and reanalyze 30 openly available empirical datasets to test for clustering. This work highlights how the Bayesian framework can embed theoretical assumptions into network models via priors and introduces a new tool for latent cluster inference in psychological network analysis.

SUMMARY Bayesian graphical modelling represents the synthesis of several recent developments in applied complex modelling. After describing a moderately challenging real example, we show how graphical models and Markov chain Monte Carlo methods naturally provide a direct path between model specification and the computational means of making inferences on that model. These ideas are illustrated with a range of modelling issues related to our example. An appendix discusses the BUGS software.

Today we are witnessing the necessity to align Business and Information Technology (IT) as well as a change in the role of IT from an enabler to an industrial sector in its own right. Based on the assumption that businesses in different sectors of the economy will require IT for different reasons and in different ways, this article introduces the EU project plugIT that aspires to develop an IT-Socket that will realize the vision of businesses "plugging-in" to IT. Three demonstration scenarios deal with: (1) "Certification" of IT infrastructure to stay compliant with regulations, (2) "Virtual Organisation" by evolving the current service orientation to a higher and more business driven abstraction as well as, (3) "Governance" of IT infrastructure introducing business context into highly distributed and complex systems. The IT-Socket follows a model-driven approach by introducing graphical modelling languages as mediators between the domain experts and IT. The research challenge is to link human interpretable graphical models – partly semi-formal – with machine interpretable semantic formalisms to enable: (1) a tighter involvement of domain experts when expressing formal knowledge specifying business requirements on IT infrastructure and services, (2) different graphical modelling languages for different views on the IT-Socket to provide modelling languages the domain expert is used to work with as well as, (3) a domain specific notation for semantics by integrating formal concepts of semantics with the graphic notation from modelling languages.

Process knowledge is the most reliable resource for qualitative modeling of complex industrial processes, which is typically expressed in natural language and stored in human brains. We thus need to capture useful connectivity and causality from such resources and convert the information into computer accessible formats. From first-principle structural models, causality can be captured and expressed as structural equations. From unstructured process knowledge and dynamic and algebraic equations, graphical models, in particular signed directed graphs and variants, can be obtained. Graphic models are widely used due to their computer tractability and human readability. Rule-based models are another alternative, which is used in expert systems. When the process information is accessible in web language, connectivity can be retrieved by query.

Graphs are used effectively in representing model structures in a variety of research fields such as statistics, artificial intelligence, data mining, biological science, medicine, decision science, educational science, etc. We use different forms of graphs according to the nature of the random variables involved. For instance, arrows are used when the relationship is asymmetric as when it is causal or temporal, and undirected edges are used when the relationship is associative. When a random field is Markov with respect to a triangulated graph, i.e., a decomposable graph, which does not have a cycle of length 4 or larger, its corresponding probability model is expressed in a factorized form which facilitates computation over the probability distribution of the random field (Kemeny et al., 1976). This computational feasibility, among others, makes such a Markov random field a most favored random field. Literature is abound in regard to the properties of theMarkov randomfield which isMarkov with respect to a decomposable graph (see Chapter 12 of Whittaker (1990) and Lauritzen (1996)). We call such a random field a decomposable graphical model. There have been remarkable improvements in learning graphical models in the form of a Bayesian network (Pearl, 1986 & 1988; Heckerman et al., 1995; Friedman & Goldszmidt, 1998; Neil et al., 1999; Neapolitan, 2004) from data. This learning however is mainly instrumented by heuristic searching algorithms and the model searching is usually NP-hard [Chickering (1996)]. A good review is given in Cooper (1999) and Neopolitan (2004) on structural discovery of Bayesian or causal networks from data. Since a Bayesian network can be transformed into a decomposable graph [Lauritzen and Spiegelhalter (1988)], the method of model combination which is proposed in this paper would lead to an improvement in graphical modelling from data. This method would be useful when we don’t have data which are large enough for the number of the random variables that are involved in the data. In this situation, it is desirable to develop marginal models of manageable sizes for subsets of variables and then search for a model for the whole set of variables based on the marginal models. The main idea of the method to be proposed is similar to constraint-based learning as described in Neapolitan (2004) (also see Meek (1995) and Spirtes et al. (2000)) where we

Graphical models are omnipresent in the software engineering field, but most current graphical modeling languages do not scale with the increasing size and complexity of today’s systems. The navigation in the diagrams becomes a major problem especially if different aspects of the system are scattered over multiple, only loosely coupled diagrams. 
\nIn this paper we present the hierarchical navigation capabilities of the Adora modeling tool. The user of this tool can freely control the level of detail in different parts of the model to reduce the size and complexity of the diagrams being displayed. Our fisheye visualization technique makes it possible to integrate all modeling aspects (structure, data, behavior, etc.) in one coherent model while keeping the size and complexity of the diagrams within reasonable limits.

Background: At present, there is an urgent problem of renovation of rolling stock characterized by a high degree in Russia. The leading position in the country's transport system belongs to railway transport. In the context of declining demand for transportation investments in railcars, which represent a significant amount of capital investment, require reasonable management decisions.
 Aim: is to work out a decision-making model for renewal the transport organization's railway rolling stock.
 Methods: of technical, economic, investment and financial analysis, decision tree, graphical modeling, system approach.
 Results: The basic components of the decision-making model are determined. The key indicators of railway rolling stock renewal are defined, reflecting the criteria for making managerial decisions in the field of operational, investment and financial activities. A graphical model is proposed that interprets the decision support system for purchasing new railcars.
 Conclusion: Using the proposed model of decision-making in the field of renovation of rolling stock allows to transfer this process to a qualitatively new level, based on the results of an objective assessment of the current and forecast state of the management object according to various alternative scenarios and based on the selection of the rational decision by comparing the expected results for each of the considered alternatives and analysis degree of their compliance with the determined goals due to its versatility and complexity.

Graphical modelling is an important tool for the efficient representation and analysis of uncertain information in knowledge-based systems. While Bayesian networks and Markov networks from probabilistic graphical modelling are well-known for a couple of years, the field of possibilistic graphical modelling occurs as a new promising area of research. Possibilistic networks provide an alternative approach compared to probabilistic networks, whenever it is necessary to model uncertainty and imprecision as two different kinds of imperfect information. Imprecision in the sense of set-valued data has often to be considered in situations where data are obtained from human observations or non-precise measurement units. In this contribution we present a comparison of the background and perspectives of probabilistic and possibilistic graphical models, and give an overview on the current state of the art of possibilistic networks with respect to propagation and learning algorithms, applicable to data mining and data fusion problems.

Recently, there has been an explosive growth in graphical modeling approaches for estimating brain functional networks. In a detailed study, we show that surprisingly, standard graphical modeling approaches for fMRI data may not yield accurate estimates of the brain network due to the inability to suitably account for temporal correlations. We propose a novel Bayesian matrix normal graphical model that jointly models the temporal covariance and the brain network under a separable structure for the covariance to obtain improved estimates. The approach is implemented via an efficient optimization algorithm that computes the maximum-a-posteriori network estimates having desirable theoretical properties and which is scalable to high dimensions. The proposed method leads to substantial gains in network estimation accuracy compared to standard brain network modeling approaches as illustrated via extensive simulations. We apply the method to resting state fMRI data from the Human Connectome Project involving a large number of time scans and brain regions, to study the relationships between fluid intelligence and functional connectivity, where it is not computationally feasible to apply existing matrix normal graphical models. Our proposed approach led to the detection of differences in connectivity between high and low fluid intelligence groups, whereas these differences were less pronounced or absent using the graphicallasso.

Deep architectures can now be well trained on massive labeled data. However, there exist many application scenarios where labeled data are sparse or absent. Domain adaptation and multi-task transfer learning provide attractive options when related labeled data or tasks are abundant from different domains. In this paper, a new graphical modeling approach to multi-layer factorization based domain adaptation is explored to address the scenarios that insufficient labeled data are available for supervised learning. A deep convolutional factorization based transfer learning (DCFTL) algorithm is proposed to facilitate layer-wise transfer learning between domains. Completely based on graphical model representation, the proposed framework can seamlessly merge inference and learning, and has clear interpretability of conditional independence. The empirical performances on image classification tasks in both supervised and semi-supervised adaptation settings illustrate the effectiveness and generalization of the proposed deep layered knowledge transfer framework.

Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary multivariate data is a major task in areas as diverse as biostatistics, econometrics, social networks, and climate data analysis. Even though time series in these applications are often non stationary, revealing interdependencies through sparse graphs has not advanced as rapidly, because estimating such time varying models is challenged by the curse of dimensionality and the associated complexity which is prohibitive. The goal of this paper is to introduce novel algorithms for joint segmentation and estimation of sparse, piecewise stationary, graphical models. The crux of the proposed approach is application of dynamic programming in conjunction with cost functions regularized with terms promoting the right form of sparsity in the right application domain. As a result, complexity of the novel schemes scales gracefully with the problem dimension.

F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults

The connection of clinical care with clinical research is the main purpose of the Learning Health System (LHS) integrating scientific information, informatics, and patient care. The LHS generates new medical knowledge as a by-product of the care process. For this purpose, the aggregation of data from Electronic Health Records (EHR), Case Report Forms (CRF), web questionnaires with other data sources like primary care databases and genetic data repositories is necessary for research purposes. This joining of healthcare and research processes results in challenges for the privacy protection framework of the LHS. Based on an exploration of EU legal requirements for data protection and privacy, different data access policies of data provider organizations as well as existing privacy frameworks of research projects, basic privacy principles and privacy requirements were extracted. Based on privacy principles and legal requirements a graphical model to display privacy protection requirements was created. This graphical model is based on concepts of requirements engineering and can be used like a model kit to create new privacy frameworks and to ease knowledge exchange with stakeholders of the LHS. Our model is built upon the concept of three privacy zones (Care Zone, Non-Care Zone and Research Zone) representing areas where similar legal requirements and rules apply. These zones contain databases, data transformation operators, such as data linkers and privacy filters and graphs to indicate the data flow necessary for research processes. The aim of the model is to help arrange its components in a way that creates a risk gradient for the data flow from a zone of high risk for patient identification to a zone of low risk. The model is applied to the analysis of several general clinical research usage scenarios and two research use cases from the TRANSFoRm project (finding patients for clinical research and linkage of databases). Both use cases represent different data collection aspects of the LHS. The model was used during discussions with data managers from the NIVEL Primary Care Database in the Netherlands and validated by representing an approved research case of using primary care data employing NIVEL services. Experiences with the graphic privacy model used to improve the privacy framework of TRANSFoRm and with the presentation of the model to LHS stakeholders and the research community are discussed.