Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction-0

<p><b>Copyright information:</b></p><p>Taken from "Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction"</p><p>http://www.biomedcentral.com/1471-2105/9/S3/S9</p><p>BMC Bioinformatics 2008;9(Suppl 3):S9-S9.</p><p>Published online 11 Apr 2008</p><p>PMCID:PMC2352866.</p><p></p

Example of the design of KART for the automatic generation of antibiotic treatment.

<p>A literature search has been performed to find treatments to treat <i>bacteremia</i> caused by <i>Pseudomonas aeruginosa</i>. The output is divided into four panels: 1) on the top-left panel, a ranked list of the most-cited antibiotics in literature is proposed; 2) on the bottom-left panel, an aggregation of these antibiotics is displayed to determine the main classes of antibiotics involved; 3) on the top-right panel, the publications supporting each antibiotic are displayed; and 4) on the bottom-right panel, an alternative way to process the literature is provided: a list of the twenty most frequent publications found by the question-answering module is displayed and shows which of the twenty first-ranked antibiotics are present in each of these publications.</p

Final results of the medical knowledge extraction module.

<p>Final results of the medical knowledge extraction module.</p

Tuning of the re-ranking experiments for the Question-Answering task.

<p>Tuning of the re-ranking experiments for the Question-Answering task.</p

System architecture of KART.

<p>On the client side, a repository containing all existing recommendations is first presented. Then, a module to edit, formalize and store the recommendation is presented. Finally, KART offers modules for the acquisition of normalized data for the parameters of the recommendation. On the server side, Java web services communicate with the MKR through SPARQL queries to extract and store recommendations. They are able to transform recommendations from Notation-3 to human-readable format and vice versa. We also propose Java web services to acquire normalized data by querying existing categorizers and a question-answering engine. On the data side, the generic semantic repository MKR is accessed. Several terminologies are necessary for categorization and normalization, and scientific libraries are used by the question-answering engine.</p

Tuning of the information retrieval parameters for the Question-Answering task.

<p>Tuning of the information retrieval parameters for the Question-Answering task.</p

Tuning of the publications filters for the Question-Answering task.

<p>Tuning of the publications filters for the Question-Answering task.</p

Schematic overview of the computational and experimental contributions of COMBREX and its users, and the interrelationships of these contributions.

<p>Data and results specific to COMBREX are shown in boxes. External data imported into COMBREX are also shown, with arrows indicating entry points into the cycle. Methodology employed by COMBREX and its users is shown in blue type, as it is used to generate data. Not shown are two critical contributions to COMBREX: genome and cluster data imported from NCBI RefSeq and ProtClustDB, respectively, and NIH funding, which enables the grants that COMBREX issues to experimental laboratories.</p

Definitions of COMBREX functional status symbols and fractions of microbial genes in COMBREX in each status category.

<p>Experimentally characterized proteins are <i>green</i>. (Those in the <i>green</i> set that have been manually curated by the GSDB are also marked with a gold “G.”) Proteins with functional predictions but no experimental evidence are <i>blue</i>. Proteins with no available functional predictions are <i>black</i>.</p