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

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

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

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

Performance of HypothesisFinder over Bioscope and Remote corpus.

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

Additional file 1: of PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain

Convergence of miRNA Expression Profiling, ?-­Synuclein Interacton and GWAS in Parkinson's Disease. (DOCX 449 KB

The MS Ontology.

<p>A) Basic formal ontology integration of MS Ontology; B) Extracted views of the MS Ontology showing the hierarchy of the concepts; C) Source documents for each category used for creating the ontology.</p