Standardized metadata collection to reinforce collaboration in Collaborative Research Centers

We present our approach for research (meta)data exchange and interconnection of scientists in medium-scale academic projects such as medical collaborative research centers (CRCs). Our webbased tool, fredato, connects established services, which we configure to a custom-tailored software bundle to match the needs of researchers. To improve collaboration, we implemented a metadata acquisition component and search function to complement the existing data management and sharing. More specifically, we enhance three points: 1. Relevant projects need to be findable to re-use data or results, avoid redundant work and improve communication among crc members. 2. The scientists also process sensitive human data for which a privacy-protected, secure exchange is critical. 3. A self-explanatory user interface is required for increased user acceptance. The main feature we present is the handling of metadata in our web application without overwhelming users through extensive and generic forms. We use flexible JSON schemas to precisely target scientists' needs of documentation and enrich them with only ontology components relevant to their use cases. The schemas are stored distributedly as datasets themselves, and automatically converted to modern web forms. Besides custom, domain-specific forms we use this editor for the addition of common metadata schemas (e.g. DataCite). We use the continuous integration capabilities of a connected Gitlab to run data-driven scripts. This includes indexing of all metadata, which makes them searchable in a structured way. Users are provided with contact information for matches and can ask to share data and results. Because project owners decide with whom to share and because datasets and metadata are bundled together, the complete dataset is always accessible (e.g. for publication at a third party repository) and full control retained by data owners. By adding this component to our tool, we provide simple, secure and searchable means for improving collaboration in the context of CRCs

Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties

The lazar framework for read across predictions was expanded for the prediction of nanoparticle toxicities, and a new methodology for calculating nanoparticle descriptors from core and coating structures was implemented. Nano-lazar provides a flexible and reproducible framework for downloading data and ontologies from the open eNanoMapper infrastructure, developing and validating nanoparticle read across models, open-source code and a free graphical interface for nanoparticle read-across predictions. In this study we compare different nanoparticle descriptor sets and local regression algorithms. Sixty independent crossvalidation experiments were performed for the Net Cell Association endpoint of the Protein Corona dataset. The best RMSE and r2 results originated from models with protein corona descriptors and the weighted random forest algorithm, but their 95% prediction interval is significantly less accurate than for models with simpler descriptor sets (measured and calculated nanoparticle properties). The most accurate prediction intervals were obtained with measured nanoparticle properties (no statistical significant difference (p < 0.05) of RMSE and r2 values compared to protein corona descriptors). Calculated descriptors are interesting for cheap and fast high-throughput screening purposes. RMSE and prediction intervals of random forest models are comparable to protein corona models, but r2 values are significantly lower

lazar-rest

<p>REST API webservice for lazar and nano-lazar. lazar (lazy structure–activity relationships) is a modular framework for read across predictions of chemical toxicities. Within the European Union’s FP7 eNanoMapper project lazar was extended with capabilities to handle nanomaterial data, interfaces to other eNanoMapper services (databases and ontologies) and a stable and user-friendly graphical interface for nanoparticle read-across predictions. <strong>lazar-rest</strong> provides a new Restful webservice to this developments.</p

qsar-report ruby gem library

<p>QMRF and QPRF reporting extension to OpenTox ruby modules and lazar.<br> The QSAR-report gem was developed to extend the lazar and nano-lazar toxicity prediction application with QMRF and QPRF reporting features.<br> The library gem is independent from lazar or nano-lazar and can also be used in any other ruby code.</p> <p><strong>About:</strong></p> <p>Classes for QMRF and QPRF reporting.</p> <ul> <li><strong>OpenTox::QMRFReport</strong>:<br> Provides a ruby OpenTox class to prepare an initial version of a QMRF report.<br> The XML output is in QMRF version 1.3 and can be finalized with the QMRF editor 2.0 ( https://sourceforge.net/projects/qmrf/ )</li> <li><strong>OpenTox::QPRFReport</strong>:<br> Provides a ruby OpenTox class to prepare an initial version of a QPRF (version 1.1) report in JSON or HTML.</li> </ul> <p><strong>Usage:</strong></p> <p><strong>QMRF </strong></p> <p>create a new QMRF report, add some content and show output:</p> <blockquote>require "qsar-report"<br> <br> # create a new report<br> report = OpenTox::QMRFReport.new<br> <br> # add a title<br> report.value "QSAR_title", "My QSAR Title"<br> <br> # change 6.2 'Available information for the training set' set inchi and smiles to Yes<br> report.change_attributes "training_set_data", {:inchi => "Yes", :smiles => "Yes"}<br> <br> # add a publication to the publication catalog<br> report.change_catalog :publications_catalog, :publications_catalog_1, {:title => "MyName M (2016) My Publication Title, QSAR News, 10, 14-22", :url => "http://myqsarnewsmag.dom"}<br> <br> # link/reference the publication to the report bibliography<br> report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_1<br> <br> # output<br> puts report.to_xml<br> <br> # validate a report (as created above) against qmrf.xsd<br> report.validate</blockquote> <p><strong>QPRF </strong></p> <p>create a new QPRF report, add some content and show output:</p> <blockquote>require "qsar-report"<br> # create a new QPRF report instance<br> report = OpenTox::QPRFReport.new<br> <br> # Set Title of the report<br> report.Title = "My QPRF Report"<br> <br> # Set Version<br> report.Version = "1"<br> <br> # Set Date<br> report.Date = Time.now.strftime("%Y/%m/%d")<br> <br> # Set the CAS number in chapter 1.1<br> report.value "1.1", "7732-18-5" # set CAS number for H²O<br> <br> # print HTML version<br> puts report.to_html<br> <br> # print formated JSON version<br> puts report.pretty_json<br>  </blockquote> <p><strong>Installation </strong></p> <blockquote>gem install qsar-report</blockquote> <p><strong>Documentation:</strong></p> <ul> <li>http://www.rubydoc.info/gems/qsar-report<br> RubyDoc.info Code documentation</li> <li>For full information on QSAR reporting see:<br> https://eurl-ecvam.jrc.ec.europa.eu/databases/jrc-qsar-model-database-and-qsar-model-reporting-formats<br> JRC QSAR Model Database and QSAR Model Reporting Formats</li> </ul

nano-lazar

<p>Graphical user interface for nano-lazar read across models. Users can predict nanoparticle toxicities by entering (i) core and coating compounds or (ii) nanoparticle properties or (iii) interactions with human serum proteins.</p> <p>According to our knowledge this is the first program that predicts nanoparticle toxicities from computed properties alone. This makes model (i) especially well suited for cheap and fast nanoparticle toxicity assessments. A detailed description of methods and validation results can be found at https://github.com/enanomapper/nano-lazar-paper/blob/master/nano-lazar.pdf.</p> <p>lazar is a modular framework for read across predictions of chemical toxicities. Within the EU FP7 eNanoMapper project lazar was extended with capabilities to handle nanomaterial data, interfaces to other eNanoMapper services (databases and ontologies) and a stable and user-friendly graphical interface for nanoparticle read-across predictions. nano-lazar is the graphical interface to nanoparticle read-across predictions.</p

Deliverable Report D3.3 Modules and services for linking and integration with third party databases

Understanding the biological effects of nanomaterials needs at least insight into the physicochemical identity; recent research has however shown how important the biological identity is in fully understanding the biological mechanisms. This requires, however, interlinking nanomaterial databases with databases from other domains. This deliverable reports on our efforts outlined in Tasks 3.4 and 3.6 to implement the Linked Data ideas to data in the nanosafety community, taking into account this recent guidance document, experimenting with a number of technical solutions to link data. We report on work that lead to the Resource Description Framework (RDF) support of the database, reusing the eNanoMapper ontology, and interlinking with other databases. We show how the RDF can be used and demonstrate its applicability with a few examples. The related deliverable D5.6 to follow is about data completeness and is also based on the output of this work

Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions

This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended

Modeling chronic toxicity : a comparison of experimental variability with (Q)SAR/read-across predictions

This study compares the accuracy of (Q)SAR/read-across predictions with theexperimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) fromin vivoexperiments. We could demonstrate that predictions of the lazy structure-activityrelationships (lazar) algorithm within the applicability domain of the trainingdata havethe same variability as the experimental training data. Predictions with a lower similaritythreshold (i.e., a larger distance from the applicability domain) are also significantly betterthan random guessing, but the errors to be expected are higher and a manual inspectionof prediction results is highly recommended

Additional file 1 of Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: an approach based on federated learning

Supplementary Material 1: Figure 1. Flow chart demonstrating the number of AATD and Non-AATD patients at each research database and the respective recording perio