Stigmatization Predicts Psychological Adjustment and Quality of Life in Children and Adolescents With a Facial Difference

Objectives This cross-sectional study assessed psychological adjustment and health-related quality of life (HRQOL) in children and adolescents with congenital or acquired facial differences and identified potential predictors of adjustment. Methods Data were obtained from 88 children, ages 9 months to 16 years, by means of parent questionnaires (n = 86) and standardized interviews with children ≥7 years old (n = 31). Evaluation measures included the Child Behavior Checklist (CBCL), KIDSCREEN-27, TNO-AZL Preschool Quality of Life Questionnaire (TAPQOL), and Perceived Stigmatization Questionnaire. Results Psychological adjustment, as measured by the CBCL, was within norms. Parent-reported HRQOL was good in preschool children. Parent- and self-reported HRQOL of participants 7-16 years old was impaired in several dimensions, including psychological well-being. Psychological adjustment (especially internalizing behavior problems) and HRQOL were predicted primarily by perceived stigmatization. Conclusions Identification of stigma experiences and appropriate support may be crucial to enhancing psychological adjustment and quality of life in children with facial disfiguremen

CheS-Mapper - Chemical Space Mapping and Visualization in 3D

Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals

Visualization and validation of (Q)SAR models

Analyzing and modeling relationships between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects in chemical datasets is a challenging task for scientific researchers in the field of cheminformatics. Therefore, (Q)SAR model validation is essential to ensure future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to approve its use in real-world scenarios as an alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model is still under discussion. In this work, we empirically compare a k-fold cross-validation with external test set validation. The introduced workflow allows to apply the built and validated models to large amounts of unseen data, and to compare the performance of the different validation approaches. Our experimental results indicate that cross-validation produces (Q)SAR models with higher predictivity than external test set validation and reduces the variance of the results. Statistical validation is important to evaluate the performance of (Q)SAR models, but does not support the user in better understanding the properties of the model or the underlying correlations. We present the 3D molecular viewer CheS-Mapper (Chemical Space Mapper) that arranges compounds in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kinds of features, like structural fragments as well as quantitative chemical descriptors. Comprehensive functionalities including clustering, alignment of compounds according to their 3D structure, and feature highlighting aid the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. Even though visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allows for the investigation of model validation results are still lacking. We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. New functionalities in CheS-Mapper 2.0 facilitate the analysis of (Q)SAR information and allow the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. Our approach reveals if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.Zusammenhänge zwischen der Struktur von chemischen Verbindungen und biologischen oder toxischen Effekten zu analysieren und zu modellieren ist eine wissenschaftliche Herausforderung im Bereich der Chemieinformatik. Deshalb ist die sorgfältige Validierung von (Q)SAR Modellen entscheidend um die Vorhersage-Genauigkeit eines Modells bei ungesehenen Verbindungen zu gewährleisten. Ordnungsgemäße Validierung ist auch eine der Voraussetzungen der Regulierungsbehörden, um den Einsatz von (Q)SAR Modellen als alternative Test-Methode von Chemikalien zu genehmigen. Allerdings wird immer noch aktiv diskutiert, welches die korrekte Validierungsmethode von (Q)SAR Modellen ist. Diese Arbeit vergleicht empirisch k-fache Kreuzvalidierung mit einer externen Validierung anhand eines Test-Datensatzes. Mit der vorgestellten Methodik werden die validierten Modelle auf große Mengen ungesehener Verbindungen angewendet, und die Genauigkeit der verschiedenen Validierungsmethoden verglichen. Unsere experimentellen Ergebnisse legen nahe, dass kreuzvalidierte (Q)SAR Modelle eine höhere Vorhersage-Genauigkeit aufweisen, als solche, die mit einem externen Testdatensatz validiert worden sind. Des weiteren ist die Varianz der Kreuzvalidierung geringer. Statistische Validierung ist zwingend notwendig, um die Vorhersage-Genauigkeit von (Q)SAR Modellen zu ermitteln. Diese Validierung ist aber nur eingeschränkt hilfreich um die Eigenschaften des Modells oder der zugrunde liegenden Beziehungen zu verstehen. In diesem Zusammenhang stellen wir den molekularen 3D-Viewer CheS-Mapper (Chemical Space Mapper) vor. Diese Computer-Anwendung ordnet chemische Verbindungen im 3D-Raum an, so dass die räumliche Distanz die Ähnlichkeit der Verbindungen widerspiegelt. Durch die Wahl der chemischen Deskriptoren kann der Benutzer die Ähnlichkeit festlegen. CheS-Mapper kann diverse Deskriptoren-Typen, zum Beispiel strukturelle Fragmente oder numerische Kennzahlen, berechnen. Des weiteren erlaubt CheS-Mapper das Clustern von Verbindungen, das Ausrichten und Übereinanderlegen der Strukturen im 3D-Raum, wie auch die farbliche Hervorhebung von Verbindungen anhand ihrer Deskriptor-Werte. Das Programm erleichtert es daher, Chemikern Muster und Zusammenhänge in den Daten zu erkennen und bekanntes wissenschaftliches Wissen zu veranschaulichen. Zwar existieren bereits einige Visualisierungs-Werkzeuge für (Q)SAR Informationen in chemischen Datensätzen, allerdings fehlt eine ganzheitliche Visualisierungs-Methode für Validierungs-Ergebnisse. Wir präsentieren visuelle Validierung, eine graphische Analyse-Methode für die Validierung eines (Q)SAR Modells mit Hilfe neuer Funktionen in CheS-Mapper 2.0. Vorhergesagte Werte für die Aktivität chemischer Verbindungen können mit tatsächlichen Aktivitäten durch die Visualisierung im 3D-Raum verglichen werden. Unser Ansatz zeigt, ob der Endpunkt zu generisch oder zu spezifisch modelliert wurde, und hebt gemeinsame Eigenschaften von falsch vorhergesagten Verbindungen hervor. Darüber hinaus können Forscher untersuchen, wie Activity Cliffs von einem Modell vorhergesagt werden. Die CheS-Mapper Software ist frei verfügbar unter http://ches-mapper.org

CheS-Mapper 2.0 for visual validation of (Q)SAR models

BACKGROUND: Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. RESULTS: We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. CONCLUSIONS: Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. GRAPHICAL ABSTRACT: Comparing actual and predicted activity values with CheS-Mapper

CheS-Mapper 2.0 for visual validation of (Q)SAR models

Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physicochemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper

Large-scale attribute selection using wrappers

Abstract—Scheme-specific attribute selection with the wrapper and variants of forward selection is a popular attribute selection technique for classification that yields good results. However, it can run the risk of overfitting because of the extent of the search and the extensive use of internal cross-validation. Moreover, althoughwrapperevaluators tendtoachievesuperior accuracy compared to filters, they face a high computational cost. The problems of overfitting and high runtime occur in particular on high-dimensional datasets, like microarray data. We investigate Linear Forward Selection, a technique to reduce the number of attributes expansions in each forward selection step. Our experiments demonstrate that this approach is faster, finds smaller subsets and can even increase the accuracy compared to standard forward selection. We also investigate a variant that applies explicit subset size determination in forward selection to combat overfitting, where the search is forced to stop at a precomputed “optimal ” subset size. We show that this technique reduces subset size while maintaining comparable accuracy. I