“Vielfalt and diversité: how local actors in France and Germany evaluate immigration and socio-cultural heterogeneity”

In both Germany and France, perceptions of immigration, diversity and their societal consequences have undergone important transformations in the past two decades. However, existing research has o

Propagating semantic information in biochemical network models

<p>Abstract</p> <p>Background</p> <p>To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.</p> <p>Results</p> <p>A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.</p> <p>Conclusions</p> <p>Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at <url>http://www.semanticsbml.org</url>.</p

The immigrant presence in collaborative structures of urban politics

Participation and representation of disadvantaged groups are important, but partly still understudied aspects of democratic politics. The present article looks at the inclusion of migrant representatives in urban governance networks making use of original survey data from 40 large cities in France and Germany. We find that about half of policy‐relevant urban actors in both countries and across cities cooperate with migrant associations regularly. This indicates that urban governance networks are furthering the civic and political presence of migrants. Cooperation with migrant associations is more likely when specific representative local institutions (foreigner/integration councils) exist, and is also boosted by the overall density of governance networks in a city. Politicians and local administrators remain central actors in such networks, while social welfare organizations emerge as important interlocutors with migrant associations. The article identifies and discusses differences between the two countries

GOrevenge: A Novel Generic Reverse Engineering Method for the Identification of Critical Molecular Players, Through the Use of Ontologies

The ever-increasing use of ontologies in modern biological analysis and interpretation facilitates the understanding of the cellular procedures, their hierarchical organization, and their potential interactions at a system's level. Currently, the gene ontology serves as a paradigm, where through the annotation of whole genomes of certain organisms, genes subsets selected, either from high-throughput experiments or with an established pivotal role regarding the probed disease, can act as a starting point for the exploration of their underlying functional interconnections. This may also aid the elucidation of hidden regulatory mechanisms among genes. Reverse engineering the functional relevance of genes to specific cellular pathways and vice versa, through the exploitation of the inner structure of the ontological vocabularies, may help impart insight regarding the identification and prioritization of the critical role of specific genes. The proposed graph-theoretical method is showcased in a pancreatic cancer and a T-cell acute lymphoblastic leukemia gene set, incorporating edge and Resnik semantic similarity metrics, and systematically evaluated regarding its performance

Integration of High-Volume Molecular and Imaging Data for Composite Biomarker Discovery in the Study of Melanoma

In this work the effects of simple imputations are studied, regarding the integration of multimodal data originating from different patients. Two separate datasets of cutaneous melanoma are used, an image analysis (dermoscopy) dataset together with a transcriptomic one, specifically DNA microarrays. Each modality is related to a different set of patients, and four imputation methods are employed to the formation of a unified, integrative dataset. The application of backward selection together with ensemble classifiers (random forests), followed by principal components analysis and linear discriminant analysis, illustrates the implication of the imputations on feature selection and dimensionality reduction methods. The results suggest that the expansion of the feature space through the data integration, achieved by the exploitation of imputation schemes in general, aids the classification task, imparting stability as regards the derivation of putative classifiers. In particular, although the biased imputation methods increase significantly the predictive performance and the class discrimination of the datasets, they still contribute to the study of prominent features and their relations. The fusion of separate datasets, which provide a multimodal description of the same pathology, represents an innovative, promising avenue, enhancing robust composite biomarker derivation and promoting the interpretation of the biomedical problem studied

Heterogeneous data fusion and selection in high-volume molecular and imaging datasets

In this work, two disparate datasets, concerning the study of the same physiological type of cutaneous melanoma but derived from different donors, one of image (dermatoscopy) and the other of molecular (trascriptomic expression) origin are utilized, so as to form an expanded in description depth, integrative dataset. Four different imputation methods are employed in order to derive the unified dataset, prior the application of backward selection together with ensemble classifiers (random forests). The various imputation schemes applied, manage to emulate the effect of biological noise on the unified dataset, adding realistic signal variation. Thus, they immunize the discovery process in the integrative dataset, from false positive artifacts, which do not have a true differential effect. The results suggest that the expansion of the feature space through the data integration and the exploitation of elaborate imputation schemes in general, aid the classification task, imparting stability as regards the derivation of the putative classifiers. © 2012 IEEE

Delineation and interpretation of gene networks towards their effect in cellular physiology- a reverse engineering approach for the identification of critical molecular players, through the use of ontologies

Exploiting ontologies, provides clues regarding the involvement of certain molecular processes in the cellular phenotypic manifestation. However, identifying individual molecular actors (genes, proteins, etc.) for targeted biological validation in a generic, prioritized, fashion, based in objective measures of their effects in the cellular physiology, remains a challenge. In this work, a new meta-analysis algorithm is proposed for the holistic interpretation of the information captured in -omic experiments, that is showcased in a transcriptomic, dynamic, DNA microarray dataset, which examines the effect of mastic oil treatment in Lewis lung carcinoma cells. Through the use of the Gene Ontology this algorithm relates genes to specific cellular pathways and vice versa in order to further reverse engineer the critical role of specific genes, starting from the results of various statistical enrichment analyses. The algorithm is able to discriminate candidate hub-genes, implying critical biochemical cross-talk. Moreover, performance measures of the algorithm are derived, when evaluated with respect to the differential expression gene list of the dataset

Inference of a robust diagnostic signature in the case of melanoma: Gene selection by information gain and gene ontology tree exploration

Integrated datasets originating from multi-modal data can be used towards the identification of causal biological actions that through a systems level process trigger the development of a disease. We use, here, an integrated dataset related to cutaneous melanoma that comes from two separate sets (microarray and imaging) and the application of data imputation methods. Our goal is to select a subset of genes that comprise candidate biomarkers and compare these to imaging features, that characterize disease at a macroscopic level. Using information gain ratio measurements and exploration of Gene Ontology (GO) tree, we identified a set of 33 genes both highly correlated to the disease status and with a central role in regulatory mechanisms. Selected genes were used to train various classifiers that could generalize well when discriminating malignant from benign melanoma samples. Results showed that classifiers performed better when selected genes were used as input, rather than imaging features selected by information gain measurements. Thus, genes in the backstage of low-level biological processes showed to carry higher information content than the macroscopic imaging features. © 2013 IEEE

Gene prioritization for inference of robust composite diagnostic signatures in the case of melanoma

An integrated dataset originating from multi-modal datasets can be used to target underlying causal biological actions that through a systems level process trigger the development of a disease. In this study, we use an integrated dataset related to cutaneous melanoma that comes from two separate sets (microarray and imaging) and the application of data imputation methods. Our goal is to associate low-level biological information, i.e. gene expression, to imaging features, that characterize disease at a macroscopic level. Using an average Spearman correlation measurement of a gene to a total of 31 imaging features, a set of 1701 genes were sorted based on their impact to imaging features. Top correlated genes, comprising a candidate set of gene biomarkers, were used to train an artificial feed forward neural network. Classification performance metrics reported here showed the proof of concept for our gene selection methodology which is to be further validated. © IFIP International Federation for Information Processing 2013