Examples with SingleFileLocationCmdLineAnnotatorSettingsConfigurer org.molgenis.data.annotation.web.settings.SingleFileLocationCmdLineAnnotatorSettingsConfigurer used on opensource projects

Example 6 with SingleFileLocationCmdLineAnnotatorSettingsConfigurer

use of org.molgenis.data.annotation.web.settings.SingleFileLocationCmdLineAnnotatorSettingsConfigurer in project molgenis by molgenis.

the class OmimAnnotator method init.

@Override
public void init() {
    List<Attribute> attributes = createOmimOutputAttributes();
    AnnotatorInfo omimInfo = AnnotatorInfo.create(AnnotatorInfo.Status.READY, AnnotatorInfo.Type.PHENOTYPE_ASSOCIATION, NAME, "OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is " + "freely available and updated daily. The full-text, referenced overviews in OMIM contain information on all " + "known mendelian disorders and over 15,000 genes. OMIM focuses on the relationship between phenotype and genotype.", attributes);
    EntityAnnotator entityAnnotator = new AbstractAnnotator(OMIM_RESOURCE, omimInfo, geneNameQueryCreator, new OmimResultFilter(entityTypeFactory, this), dataService, resources, new SingleFileLocationCmdLineAnnotatorSettingsConfigurer(OMIM_LOCATION, omimAnnotatorSettings)) {

        @Override
        public List<Attribute> createAnnotatorAttributes(AttributeFactory attributeFactory) {
            return createOmimOutputAttributes();
        }
    };
    annotator.init(entityAnnotator);
}

Example 7 with SingleFileLocationCmdLineAnnotatorSettingsConfigurer

use of org.molgenis.data.annotation.web.settings.SingleFileLocationCmdLineAnnotatorSettingsConfigurer in project molgenis by molgenis.

the class DannAnnotator method init.

@Override
public void init() {
    List<Attribute> attributes = createDannOutputAttributes();
    AnnotatorInfo dannInfo = AnnotatorInfo.create(AnnotatorInfo.Status.READY, AnnotatorInfo.Type.PATHOGENICITY_ESTIMATE, NAME, "Annotating genetic variants, especially non-coding variants, " + "for the purpose of identifying pathogenic variants remains a challenge." + " Combined annotation-dependent depletion (CADD) is an al- gorithm designed " + "to annotate both coding and non-coding variants, and has been shown to outper- form " + "other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) " + "to dif- ferentiate evolutionarily derived, likely benign, alleles from simulated, " + "likely deleterious, variants. However, SVMs cannot capture non-linear relationships" + " among the features, which can limit performance. To address this issue, we have" + " developed DANN. DANN uses the same feature set and training data as CADD to train" + " a deep neural network (DNN). DNNs can capture non-linear relation- ships among " + "features and are better suited than SVMs for problems with a large number of samples " + "and features. We exploit Compute Unified Device Architecture-compatible " + "graphics processing units and deep learning techniques such as dropout and momentum " + "training to accelerate the DNN training. DANN achieves about a 19%relative reduction " + "in the error rate and about a 14%relative increase in the area under the curve (AUC) metric " + "over CADD’s SVM methodology. " + "All data and source code are available at https://cbcl.ics.uci.edu/ public_data/DANN/.", attributes);
    EntityAnnotator entityAnnotator = new AbstractAnnotator(DANN_TABIX_RESOURCE, dannInfo, new LocusQueryCreator(vcfAttributes), new MultiAllelicResultFilter(attributes, vcfAttributes), dataService, resources, new SingleFileLocationCmdLineAnnotatorSettingsConfigurer(DANN_LOCATION, dannAnnotatorSettings)) {

        @Override
        public List<Attribute> createAnnotatorAttributes(AttributeFactory attributeFactory) {
            return createDannOutputAttributes();
        }
    };
    annotator.init(entityAnnotator);
}