Application of two machine learning algorithms to genetic association studies in the presence of covariates

BACKGROUND: Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. METHODS AND RESULTS: In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. CONCLUSION: Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation

A Meta-analysis of Attachment to Parents and Delinquency

To investigate the link between attachment to parents and delinquency, and the potential moderating effects of age and sex, 74 published and unpublished manuscripts (N = 55,537 participants) were subjected to a multilevel meta-analysis. A mean small to moderate effect size was found (r = 0.18). Poor attachment to parents was significantly linked to delinquency in boys and girls. Stronger effect sizes were found for attachment to mothers than attachment to fathers. In addition, stronger effect sizes were found if the child and the parent had the same sex compared to cross-sex pairs of children and parents. Age of the participants moderated the link between attachment and delinquency: larger effect sizes were found in younger than in older participants. It can be concluded that attachment is associated with juvenile delinquency. Attachment could therefore be a target for intervention to reduce or prevent future delinquent behavior in juveniles

Social, Structural and Behavioral Determinants of Overall Health Status in a Cohort of Homeless and Unstably Housed HIV-Infected Men

Background: Previous studies indicate multiple influences on the overall health of HIV-infected persons; however, few assess and rank longitudinal changes in social and structural barriers that are disproportionately found in impoverished populations. We empirically ranked factors that longitudinally impact the overall health status of HIV-infected homeless and unstably housed men. Methods and Findings: Between 2002 and 2008, a cohort of 288 HIV+ homeless and unstably housed men was recruited and followed over time. The population was 60 % non-Caucasian and the median age was 41 years; 67 % of study participants reported recent drug use and 20 % reported recent homelessness. At baseline, the median CD4 cell count was 349 cells/ml and 18 % of eligible persons (CD4,350) took antiretroviral therapy (ART). Marginal structural models were used to estimate the population-level effects of behavioral, social, and structural factors on overall physical and mental health status (measured by the SF-36), and targeted variable importance (tVIM) was used to empirically rank factors by their influence. After adjusting for confounding, and in order of their influence, the three factors with the strongest negative effects on physical health were unmet subsistence needs, Caucasian race, and no reported source of instrumental support. The three factors with the strongest negative effects on mental health were unmet subsistence needs, not having a close friend/confidant, and drug use. ART adherence.90 % ranked 5th for its positive influence on mental health, and viral loa

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present <it>clusterMaker</it>, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. <it>clusterMaker </it>is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.</p> <p>Results</p> <p>Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast <it>Saccharomyces cerevisiae</it>; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.</p> <p>Conclusions</p> <p>The Cytoscape plugin <it>clusterMaker </it>provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the <it>clusterMaker </it>plugin. <it>clusterMaker </it>is available via the Cytoscape plugin manager.</p

Spectral hole burning: examples from photosynthesis

The optical spectra of photosynthetic pigment–protein complexes usually show broad absorption bands, often consisting of a number of overlapping, ‘hidden’ bands belonging to different species. Spectral hole burning is an ideal technique to unravel the optical and dynamic properties of such hidden species. Here, the principles of spectral hole burning (HB) and the experimental set-up used in its continuous wave (CW) and time-resolved versions are described. Examples from photosynthesis studied with hole burning, obtained in our laboratory, are then presented. These examples have been classified into three groups according to the parameters that were measured: (1) hole widths as a function of temperature, (2) hole widths as a function of delay time and (3) hole depths as a function of wavelength. Two examples from light-harvesting (LH) 2 complexes of purple bacteria are given within the first group: (a) the determination of energy-transfer times from the chromophores in the B800 ring to the B850 ring, and (b) optical dephasing in the B850 absorption band. One example from photosystem II (PSII) sub-core complexes of higher plants is given within the second group: it shows that the size of the complex determines the amount of spectral diffusion measured. Within the third group, two examples from (green) plants and purple bacteria have been chosen for: (a) the identification of ‘traps’ for energy transfer in PSII sub-core complexes of green plants, and (b) the uncovering of the lowest k = 0 exciton-state distribution within the B850 band of LH2 complexes of purple bacteria. The results prove the potential of spectral hole burning measurements for getting quantitative insight into dynamic processes in photosynthetic systems at low temperature, in particular, when individual bands are hidden within broad absorption bands. Because of its high-resolution wavelength selectivity, HB is a technique that is complementary to ultrafast pump–probe methods. In this review, we have provided an extensive bibliography for the benefit of scientists who plan to make use of this valuable technique in their future research

Оценка качества образования на основе компетентностного подхода

В работе представлен практический опыт оценки качества образования в новом формате компетентностного подход