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. 2017 Jul 5;12(7):e0180539.
doi: 10.1371/journal.pone.0180539. eCollection 2017.

Enriching plausible new hypothesis generation in PubMed

Seung Han Baek  1 , Dahee Lee  2 , Minjoo Kim  3 , Jong Ho Lee  3 , Min Song  2
Affiliations

Enriching plausible new hypothesis generation in PubMed

Seung Han Baek et al. PLoS One. .

Abstract

Background: Most of earlier studies in the field of literature-based discovery have adopted Swanson's ABC model that links pieces of knowledge entailed in disjoint literatures. However, the issue concerning their practicability remains to be solved since most of them did not deal with the context surrounding the discovered associations and usually not accompanied with clinical confirmation. In this study, we aim to propose a method that expands and elaborates the existing hypothesis by advanced text mining techniques for capturing contexts. We extend ABC model to allow for multiple B terms with various biological types.

Results: We were able to concretize a specific, metabolite-related hypothesis with abundant contextual information by using the proposed method. Starting from explaining the relationship between lactosylceramide and arterial stiffness, the hypothesis was extended to suggest a potential pathway consisting of lactosylceramide, nitric oxide, malondialdehyde, and arterial stiffness. The experiment by domain experts showed that it is clinically valid.

Conclusions: The proposed method is designed to provide plausible candidates of the concretized hypothesis, which are based on extracted heterogeneous entities and detailed relation information, along with a reliable ranking criterion. Statistical tests collaboratively conducted with biomedical experts provide the validity and practical usefulness of the method unlike previous studies. Applying the proposed method to other cases, it would be helpful for biologists to support the existing hypothesis and easily expect the logical process within it.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Open (left) and closed (right) discovery process defined by Weeber at al. [16].
Fig 2
Fig 2. Extension of Swanson’s ABC model.
Fig 3
Fig 3. Overview of our proposed approach.
Fig 4
Fig 4. Visualization of a portion of the directed network generated by literature mining.
Fig 5
Fig 5. Statistical relations of lactosylceramide, nitric oxide, malondialdehyde, and ba-PWV.
Relationship of lactosylceramide (d18:1/12:0), nitric oxide, malondialdehyde, and ba-PWV in male subjects under 50 yrs.*Tested by log-transformed. Tested by Pearson correlation (r0: smoker, r1: non-smoker, r2: total). (A) r0 = -0.739, P0<0.001; r1 = -0.388, P1 = 0.061; r2 = -0.551, P2<0.001. (B) r0 = -0.751, P0<0.001; r1 = -0.400, P1 = 0.053; r2 = -0.612, P2<0.001. (C) r0 = 0.526, P0 = 0.012; r1 = 0.628, P1 = 0.001; r2 = 0.570, P2<0.001. (D) r0 = 0.527, P0 = 0.012; r1 = 0.414, P1 = 0.044; r2 = 0.470, P2 = 0.001.
Fig 6
Fig 6. Overall view of nitric oxide, malondialdehyde, and ba-PWV with lactosylceramade.
Fig 7
Fig 7. Network relations of lactosylceramade, nitric oxide, malondialdehyde, and arterial stiffness.
Fig 8
Fig 8. Scatterplot of database-based versus semantic relatedness score (both normalized).
See this image and copyright information in PMC

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