An overview of HypothesisFinder development approach.

<p>The workflow for the development of HypothesisFinder shows how the model was trained, optimized and on what data sets its performance was evaluated.</p

Performance of HypothesisFinder on the HYPO–TEST corpora.

<p>MaxEnt indicates Maximum Entropy classifier. Applied features sets were baseline features (<i>base</i>), speculative features (<i>spec</i>), lexico-syntactic features (<i>lex</i>), and their combinations.</p

Comparison of information densities: HypothesisFinder vs.

<p><b>AlzSWAN.</b> A- The statistical comparison between the numbers of hypotheses related to AD captured by HypothesisFinder within SCAIView (s<i>tage-specific retrieval</i>) and the hypotheses with extended annotation derived from citations mentioned in the AlzSWAN database. B- A comparison between biological entity retrieval using SCAIView and relevant entries in AlzSWAN.</p

Chronological order of hypotheses proposed in Mild (A), Moderate (B) and Severe (C) AD.

<p>Figure shows a schematic representation of how AD stage specific hypothesis related to top five genes that are high-frequently investigated in the literature has evolved in number over time Abbreviations mentioned stands for Amyloid beta (A4) precursor protein (APP), Apolipoprotein E (APOE), Microtubule- associated protein tau (MAPT), Choline O-acetyltransferase (CHAT), Brain-derived neurotrophic factor (BDNF), Beta-site APP-cleaving enzyme 1(BACE 1), Galanin prepropedtide (GAL).</p

Stage specific AD networks.

<p>Figure presents protein interaction networks for Mild(A), Moderate(B), Severe(C) stage of Alzheimer's disease. These stage specific networks have been generated by using BioNetBuilder plugin in Cytoscape, which was given genes and proteins, associated to stage-wise hypotheses as input.</p

Performance of HypothesisFinder over Bioscope and Remote corpus.

<p>Performance of HypothesisFinder over Bioscope and Remote corpus.</p

Example showing usage of HypothesisFinder integrated in SCAIView for extracting hypotheses related to Alzheimer's disease.

<p>Figure shows how HypothesisFinder is used within SCAIView in conjugation with other pre-indexed terminologies and ontologies to retrieve Alzheimer-specific hypotheses. Presented example shows how a hypothesis positioning Tau and Amyloid-beta as potential biomarker candidates in relation to AD is identified by HypothesisFinder in scientific abstracts.</p

Classification of speculative patterns.

<p>Figure presents examples of strong, moderate and weak speculative patterns along with their estimated ‘percent efficacy’ or ability of pattern to cast a sentence as speculative.</p

OSIRISv1.2: A named entity recognition system for sequence variants of genes in biomedical literature-0

On for human Gene and dbSNP databases from the NCBI. The starting point of the system is a gene, for which a set of articles is annotated using the NER tool ProMiner and stored in the TextMiningDB (1). The gene-specific corpus is accessed by OSIRISv1.2 (2) to obtain the MEDLINE citations annotated to a NCBI Gene entry. The corresponding MEDLINE abstracts are retrieved from a local repository (3). In addition, sequence data for each gene and its sequence variants are retrieved from HgenetInfoDB, and this information is used to generate the SNP terminology (4). The next step of OSIRISv1.2 is the search for occurrences of the sequence variant terms in each gene-specific corpus by processing the MEDLINE abstracts. This information (SNP-specific corpus) is returned to the TextMiningDB database (5). The results of OSIRISv1.2, stored in the TextMiningDB, can be accessed through our web interface at [25]. GenDB: data retrieval system used for conversion of the XML files to the files in MEDLINE format, indexing of the MEDLINE files and for their retrieval (internal development of FhG-SCAI by Theo-Heinz Mevissen). ProMiner has been described elsewhere [15]. For simplicity we use in this figure the term SNP to refer to all variations present in the database (SNPs and other types of sequence variants).<p><b>Copyright information:</b></p><p>Taken from "OSIRISv1.2: A named entity recognition system for sequence variants of genes in biomedical literature"</p><p>http://www.biomedcentral.com/1471-2105/9/84</p><p>BMC Bioinformatics 2008;9():84-84.</p><p>Published online 5 Feb 2008</p><p>PMCID:PMC2277400.</p><p></p

Additional file 2: of NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

The developed RDF models and the SPARQL queries used are made available at: http://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/neurordf.html . (ZIP 178 kb