Information network contains abundant knowledge about relationships among people or entities. Unfortunately, such kind of knowledge is often hidden in a network where different kinds of relationships are not explicitly categorized. For example, in a research publication network, the advisor-advisee relationships among researchers are hidden in the coauthor network. Discovery of those relationships can benefit many interesting applications such as expert finding and research community analysis. In this paper, we take a computer science bibliographic network as an example, to analyze the roles of authors and to discover the likely advisoradvisee relationships. In particular, we propose a time-constrained probabilistic factor graph model (TPFG), which takes a research publication network as input and models the advisor-advisee relationship mining problem using a jointly likelihood objective function. We further design an efficient learning algorithm to optimize the objective function. Based on that our model suggests and ranks probable advisors for every author. Experimental results show that the proposed approach infer advisor-advisee relationships efficiently and achieves a state-of-the-art accuracy (80-90%). We also apply the discovered advisor-advisee relationships to a specific expert finding task and empirical study shows that the search performance can be effectively improved (+4.09% by NDCG@5).

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This paper introduces the R package meta4diag for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package meta4diag is a purpose-built front end of the R package INLA. While INLA offers full Bayesian inference for the large set of latent Gaussian models using integrated nested Laplace approximations, meta4diag extracts the features needed for bivariate meta-analysis and presents them in an intuitive way. It allows the user a straightforward modelspecification and offers user-specific prior distributions. Further, the newly proposed penalised complexity prior framework is supported, which builds on prior intuitions about the behaviours of the variance and correlation parameters. Accurate posterior marginal distributions for sensitivity and specificity as well as all hyperparameters, and covariates are directly obtained without Markov chain Monte Carlo sampling. Further, univariate estimates of interest, such as odds ratios, as well as the SROC curve and other common graphics are directly available for interpretation. An interactive graphical user interface provides the user with the full functionality of the package without requiring any R programming. The package is available through CRAN https://cran.r-project.org/web/packages/meta4diag/ and its usage will be illustrated using three real data examples.

In a bivariate meta-analysis, the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Using Bayesian inference is particularly attractive as informative priors that add a small amount of information can stabilise the analysis without overwhelming the data. However, Bayesian analysis is often computationally demanding and the selection of the prior for the covariance matrix of the bivariate structure is crucial with little data. The integrated nested Laplace approximations method provides an efficient solution to the computational issues by avoiding any sampling, but the important question of priors remain. We explore the penalised complexity (PC) prior framework for specifying informative priors for the variance parameters and the correlation parameter. PC priors facilitate model interpretation and hyperparameter specification as expert knowledge can be incorporated intuitively. We conduct a simulation study to compare the properties and behaviour of differently defined PC priors to currently used priors in the field. The simulation study shows that the PC prior seems beneficial for the variance parameters. The use of PC priors for the correlation parameter results in more precise estimates when specified in a sensible neighbourhood around the truth. To investigate the usage of PC priors in practice, we reanalyse a meta-analysis using the telomerase marker for the diagnosis of bladder cancer and compare the results with those obtained by other commonly used modelling approaches. Copyright © 2017 John Wiley & Sons, Ltd.

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Many studies have examined the association between paraoxonase 1 (PON1) -L55M polymorphisms and risk of coronary heart disease (CHD), but the results remained inconsistent. We therefore aimed to address this association by performing an updated meta-analysis in the Chinese population. The PubMed, EMBASE, Web of Science, and Chinese National Knowledge Infrastructure were searched up to May 2020. The strength of statistical association was assessed with odds ratio (OR) and 95% confidence interval (CI). A total of eight studies with 1826 CHD cases and 1817 controls were finally included in the analysis. In the overall and subgroup analyses by control sources and geographic areas, the results showed no significant associations with CHD among all analysis models. Furthermore, we performed the analysis by including or excluding the HWE-violating studies. The results suggested that the MM genetype were significantly associated with CHD in studies not consistent with HWE under recessive and dominant models. This meta-analysis demonstrates that the PON1 -L55M polymorphism may not be associated with CHD risk in the Chinese population. Further studies with strict selection of patients and controls in different ethnic populations will be required to clarify this finding.

Based on the theory of system dynamics, a system dynamics model regarding to housing price is constructed. The changes on housing price are simulated via Vensim DSS software, and the effectiveness of the model is verified. Policy regulation experiments are conducted from the prospective of population, land and taxation that are mostly concerned by the government. It can be concluded that the land policy and taxation policy are the main methods for regulation. It provides macro-direction for the government to manage the commercial housing market and control the price of commercial residential house, as well as corresponding decision basis for house purchasers and commercial housing developers in their purchasing and investing. It also provides a systematic prediction method for the comprehensive simulation system of the commercial housing market.