Enriched annotation

The ontology that underlies the information extraction and annotation process is solely based on taxonomic relationships. We intend to enrich our ontology with typed relationships. We are currently evaluating how typed relationships can extend the functionality of the UltraLink and how the expressivity for our ontology impacts the computational complexity of formal reasoning [9]. 

Task General annotation of significantly enriched regions. 

Table 7.1 Annotation and general statistics of enriched regions.

Method Use RegionMiner to compare the annotated TSR with the modifications in a window of —10 000 to +10 000 bp around the enriched regions (Figure 7.5). 

The expert can now annotate and enrich the document and the structural information of the new extruder realization. The categorization schemes can be used for this purpose. 

After these activities, the project file is stored in the DMS, while its structural content (the realization of the extruder) is stored inside the PDW repository, together with additional information like domain model annotations and enrichments. Importing of information into the PDW is done via an XML format that is directly based on, and thus convertible into, the core ontology concepts. External resources need to be transformed into this format, based on the rules defined by derivations of the Core Ontology s Transformation concept, and implemented e.g. by XML stylesheet transformations (see Subsect. 4.1.5). 

The same flow of activities can be used with any different tool and document information. Depending on the degree of integration, the document content may be converted into the ontological format of the PDW (see also Subsect. 3.2.1 about fine-grained product relationships and conversions), or enriched and annotated based on a coarse-grained categorization model. While the former has been applied in the scenario described here, the latter concept is used in the TRAMP tool as described in Subsect. 4.1.4. 

A relation is modeled as a node in order to enable attributed links between entities. Furthermore, relations are binary and directed. They are connected with their source and target entity via a corresponding Source and Target edge. Further specializations of the node class Relationship are e.g. the node types Association, Integrates, and Uses. Supplementary, an Association is extended with a RelationshipEnd for the source as well as for the target entity to allow the annotation of multiplicities for that relation. As a RelationshipEnd is also derived from the node class ModelElement, its name attribute can be used to enrich the source and the target entity with a role identifier. 

Note that randomizations are highly recommended, but not always necessary. As described in the introduction, the number of conserved fc-mers to retain for further investigation can be selected on the basis of independent validation data, such as gene expression, functional annotation enrichment, or complementarily to microRNAs (1,3). 

Instead of applying additional filtering mechanisms on a hit list, one can evaluate the quality of the pharmacophores to be used as 3D search queries beforehand. The assessment of the quality of a 3D pharmacophore will strictly depend on the main objective(s) of the project (coverage and/or selectivity and/or enrichment). To evaluate one or multiple pharmacophores, one requires a test database that contains i) multiple families of molecules (diverse in structure and type of activity) and ii) a clear activity annotation (if possible with the molecular mechanism of action). Giiner and Henry [44] have developed a series of indices that can be calculated for multiple pharmacophores in order to make an informed decision about the one to be used to screen a virtual library or a corporate collection. 

An example of the outcome of a Fisher exact test is shown in Figure 4.5. A set of MMoA-annotated compounds were screened for inhibition of TNF-a production in lipopolysaccharide-stimulated THP-1 cells. The TNF-a pathway is well characterized and many known protein nodes, such as IKBKB, were identified in the analysis. Target discovery was consistent with a pathway enrichment analysis and, for each protein, a contigency table was constructed with /i-value and odds ratio calculated. To make sure that the / -values identified were significant when testing many different targets, a Bonferroni correction term - a method used to counteract the problem of multiple comparisons, which occurs when a set of statistical inferences are considered simultaneously - was applied. 

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