MOESM1 of Ambiguity of non-systematic chemical identifiers within and between small-molecule databases

Additional file 1. The effect of all standardization settings on reducing ambiguity of non-systematic identifiers across databases

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining

<p>Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships.</p

Internal platform implementation software overview.

<p>Used components are implemented in Java, leveraging the open-source nature of this solution. 1) Platform engine components include Hibernate, JPA, Spring Security, POI, Log4j, Guice and custom code to control the application and serve it as a Tomcat web application. 2) The Web engine relies on Google Web Toolkit to generate a highly responsive web workspace. Add-ons such as GXT and Gin were used to improve the user interactions’ performance and reliability.</p

EU-ADR initiative data flow.

<p>1) Data extracted from electronic health record (EHR) resources are semantically harmonized for data mining, generating a raw drug-event pair list. 2) The signal substantiation process analyses the submitted data, re-ranking the signal list, based on multiple algorithms. 3) Users trigger data analysis and exploration to validate the system operability.</p

EU-ADR Web Platform workspace interface for an undisclosed drug (<i>XYZ</i>) exploration scenario containing the signal list that results from distributed knowledge provider algorithm outputs and evidence combination statistical analysis.

<p>Workflow results are labelled with Y in case sufficient evidence is found to support a potential drug-event relationship, or N otherwise. Evidence combination yields a score of H, M or L, indicating High, Moderate or Low risk respectively, of a drug-event relationship being in fact an ADR signal.</p

Additional file 1: of A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy

Table S1. Coding variants under the linkage peaks for LVH proxy measurements. Table S2. Selected damaging variants in the coding regions contained in the linkage regions. Table S3. SKAT and burden tests for genes of interest. Table S4. Results of linkage analyses before (LOD1) and after (LOD2) regression on GWAS SNPs under the linkage peaks. Table S5. Descriptive statistics of the Rotterdam study population. Table S6. Replications results in the Rotterdam Study. Figure S1. Venn diagram showing the overlap between the different ERF genotyping experiments. Figure S2. Pedigrees segregating rs138968470. (DOCX 119 kb

Three-tier triage system (detection, filtering, and substantiation) for detecting ‘prime suspects’.

<p>Three-tier triage system (detection, filtering, and substantiation) for detecting ‘prime suspects’.</p

Characteristics of the databases in the EU-ADR network.

<p><b><u>Legend:</u></b></p><p><b>ICPC</b>: International Classification of Primary Care.</p><p><b>ICD9-CM</b>: International Classification of Diseases –9th revision Clinical Modification.</p><p><b>RCD</b>: READ CODE Classification.</p><p><b>ICD-10</b>: International Classification of Diseases –10th revision.</p><p><b>MINSAN</b>: Italian Ministry of Health.</p><p><b>NOTE</b>: * QRESEARCH did not contribute data for the analyses described in this paper.</p

Table 3. ‘Prime suspects’: drugs potentially associated with increased risk of acute myocardial infarction which passed the filtering (i.e. novelty) and substantiation (i.e. biological plausibility) criteria.

<p>Table 3. ‘Prime suspects’: drugs potentially associated with increased risk of acute myocardial infarction which passed the filtering (i.e. novelty) and substantiation (i.e. biological plausibility) criteria.</p

Schematic representation of the process of substantiation of suspected drug-induced adverse events via proteins (A) and via pathways (B).

<p>(From Bauer-Mehren A, van Mulligen EM, Avillach P, Carrascosa Mdel C, Garcia-Serna R, et al. (2012) Automatic filtering and substantiation of drug safety signals. <i>PLoS Comput Biol 8: e1002457</i>. Reproduced with permission from the authors).</p