Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA–CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA–CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA–CMI–OI, for inferring GRNs. After imputation of missing data, the performances of PCA–CMI and PCA–CMI–OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA–CMI–OI algorithm outperform other imputation methods and PCA–CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.     

Confirmatory Factor Analysis of Persian Adaptation of Multidimensional Students’ Life Satisfaction Scale (MSLSS)

Validity and reliability of Persian adaptation of MSLSS in the 12–18 years, middle and high school students (430 students in grades 6–12 in Bushehr port, Iran) using confirmatory factor analysis by means of LISREL statistical package were checked. Internal consistency reliability estimates (Cronbach’s coefficient α) were all above the conventional criterion of 0.70 for all factors indicating adequate reliability of the factors. Cronbach’s coefficient α of the total scale was 0.83. The pattern matrix appeared by and large to be consistent with previous investigations of the MSLSS using exploratory factor analytic methods. It seems this Persian adaptation of the MSLSS offers a reliable and valid means of assessing Iranian middle and high school students’ life satisfaction. Using that as a screening tool in schools may help in discriminating between different domains responsible for child and youth problems.     

Sexually transmitted disease services at US colleges and universities

The authors' objectives in this study were to describe the proportion of schools providing and the percentage of students with access to HIV and sexually transmitted disease (STD) education, treatment, and prevention services at 2-year and 4-year US colleges and universities. The authors mailed self-administered questionnaires to a stratified random sample (n = 910) of the 2,755 US schools with an enrollment of more than 500 students; 736 (81%) returned the survey. Four hundred seventy-four schools (60%) had a health center, representing 73% of students. Schools with a health center or housing for students were more likely to provide STD education; 52% of the schools made condoms available to students. Sixty percent of schools with health centers could test for both Chlamydia trachomatis and Neisseria gonorrhoeae, but only 67% of these schools screened women for these infections. Although most schools provided some prevention education, access to prevention, testing, and education should be increased at schools where these services are possible but not available.     

A new multivariate imputation method based on Bayesian networks

Dealing with incomplete data is a pervasive problem in statistical surveys. Bayesian networks have been recently used in missing data imputation. In this research, we propose a new methodology for the multivariate imputation of missing data using discrete Bayesian networks and conditional Gaussian Bayesian networks. Results from imputing missing values in coronary artery disease data set and milk composition data set as well as a simulation study from cancer-neapolitan network are presented to demonstrate and compare the performance of three Bayesian network-based imputation methods with those of multivariate imputation by chained equations (MICE) and the classical hot-deck imputation method. To assess the effect of the structure learning algorithm on the performance of the Bayesian network-based methods, two methods called Peter-Clark algorithm and greedy search-and-score have been applied. Bayesian network-based methods are: first, the method introduced by Di Zio et al. [Bayesian networks for imputation, J. R. Stat. Soc. Ser. A 167 (2004), 309–322] in which, each missing item of a variable is imputed using the information given in the parents of that variable; second, the method of Di Zio et al. [Multivariate techniques for imputation based on Bayesian networks, Neural Netw. World 15 (2005), 303–310] which uses the information in the Markov blanket set of the variable to be imputed and finally, our new proposed method which applies the whole available knowledge of all variables of interest, consisting the Markov blanket and so the parent set, to impute a missing item. Results indicate the high quality of our new proposed method especially in the presence of high missingness percentages and more connected networks. Also the new method have shown to be more efficient than the MICE method for small sample sizes with high missing rates.