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.2013;9(7):e1003117.
doi: 10.1371/journal.pcbi.1003117. Epub 2013 Jul 25.

'HypothesisFinder:' a strategy for the detection of speculative statements in scientific text

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'HypothesisFinder:' a strategy for the detection of speculative statements in scientific text

Ashutosh Malhotra et al. PLoS Comput Biol.2013.

Abstract

Speculative statements communicating experimental findings are frequently found in scientific articles, and their purpose is to provide an impetus for further investigations into the given topic. Automated recognition of speculative statements in scientific text has gained interest in recent years as systematic analysis of such statements could transform speculative thoughts into testable hypotheses. We describe here a pattern matching approach for the detection of speculative statements in scientific text that uses a dictionary of speculative patterns to classify sentences as hypothetical. To demonstrate the practical utility of our approach, we applied it to the domain of Alzheimer's disease and showed that our automated approach captures a wide spectrum of scientific speculations on Alzheimer's disease. Subsequent exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches, and can thus provide added value to ongoing research activities.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. An overview of HypothesisFinder development approach.
The workflow for the development of HypothesisFinder shows how the model was trained, optimized and on what data sets its performance was evaluated.
Figure 2
Figure 2. Classification of speculative patterns.
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.
Figure 3
Figure 3. Example showing usage of HypothesisFinder integrated in SCAIView for extracting hypotheses related to Alzheimer's disease.
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.
Figure 4
Figure 4. Comparison of information densities: HypothesisFinder vs.
AlzSWAN. A- The statistical comparison between the numbers of hypotheses related to AD captured by HypothesisFinder within SCAIView (stage-specific retrieval) 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.
Figure 5
Figure 5. Chronological order of hypotheses proposed in Mild (A), Moderate (B) and Severe (C) AD.
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).
Figure 6
Figure 6. Stage specific AD networks.
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.
See this image and copyright information in PMC

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This work was supported by the B-IT foundation (http://www.b-it-center.de/) and a scholarship to AM (Scholarship PLUS program of the State of NorthRhine Westphalia). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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