Демографічні фактори розвитку соціального капіталу

У статті проаналізовано тенденції змін основних демографічних показників в Україні й Донецькому регіоні зокрема, показано вплив цих факторів на розвиток соціального капіталу. Акцентовано, що дослідження демографічних факторів формування соціального капіталу на державному і регіональному рівнях дозволяє відповідним державним органам управління отримувати повну інформацію стосовно будь-яких змін демографічного розвитку та вживати заходів з оптимізації параметрів трудового та інтелектуального потенціалу населення.In the article the tendencies of changes of basic demographic indicators are analysed in Ukraine and Donetsk region in particular, influence of these factors is rotined on development of social capital. It is accented, that research of demographic factors of forming of social capital on state and regional levels allows the proper public organs of management to get complete information on any changes of demographic development and take measures from optimization of parameters of labour and intellectual potential of population

On identifiability of certain latent class models

Blischke [1962. Moment estimators for the parameters of a mixture of two binomial distributions. Ann. Math. Statist. 33, 444-454] studies a mixture of two binomials, a latent class model. In this article we generalize this model to a mixture of two products of binomials. We show when this generalized model is identifiable

Statistical analysis of the cancer cell's molecular entropy using high-throughput data

Motivation: As cancer progresses, DNA copy number aberrations accumulate and the genomic entropy (chromosomal disorganization) increases. For this surge to have any oncogenetic effect, it should (to some extent) be reflected at other molecular levels of the cancer cell, in particular that of the transcriptome. Such a coincidence of cancer progression and the propagation of an entropy increase through the molecular levels of the cancer cell would enhance the understanding of cancer evolution. Results: A statistical argument reveals that (under some assumptions) an entropy increase in one random variable (DNA copy number) leads to an entropy increase in another (gene expression). Statistical methodology is provided to investigate the relation between the genomic and transcriptomic entropy using high-throughput data. Analyses of multiple high-throughput datasets using this methodology show a close, concordant relation among the genomic and transcriptomic entropy. Hence, as cancer evolves, and the genomic entropy increases, the transcriptomic entropy is also expected to surge. © The Author 2010. Published by Oxford University Press. All rights reserved

Penalized differential pathway analysis of integrative oncogenomics studies

Through integration of genomic data from multiple sources, we may obtain a more accurate and complete picture of the molecular mechanisms underlying tumorigenesis. We discuss the integration of DNA copy number and mRNA gene expression data from an observational integrative genomics study involving cancer patients. The two molecular levels involved are linked through the central dogma of molecular biology. DNA copy number aberrations abound in the cancer cell. Here we investigate how these aberrations affect gene expression levels within a pathway using observational integrative genomics data of cancer patients. In particular, we aim to identify differential edges between regulatory networks of two groups involving these molecular levels. Motivated by the rate equations, the regulatory mechanism between DNA copy number aberrations and gene expression levels within a pathway is modeled by a simultaneous-equations model, for the one- and two-group case. The latter facilitates the identification of differential interactions between the two groups. Model parameters are estimated by penalized least squares using the lasso (L1) penalty to obtain a sparse pathway topology. Simulations show that the inclusion of DNA copy number data benefits the discovery of gene-gene interactions. In addition, the simulations reveal that cis-effects tend to be over-estimated in a univariate (single gene) analysis. In the application to real data from integrative oncogenomic studies we show that inclusion of prior information on the regulatory network architecture benefits the reproducibility of all edges. Furthermore, analyses of the TP53 and TGFb signaling pathways between ER+ and ER- samples from an integrative genomics breast cancer study identify reproducible differential regulatory patterns that corroborate with existing literature