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Inferring Drug-Protein–Side Effect Relationships from Biomedical Text

1
Department of Library and Information Science, Yonsei University, Seoul 03722, Korea
2
Institute of Convergence, Yonsei University, Seoul 03722, Korea
3
Department of Creative IT Engineering, POSTECH, Pohang 37673, Korea
*
Author to whom correspondence should be addressed.
Genes 2019, 10(2), 159; https://doi.org/10.3390/genes10020159
Received: 24 January 2019 / Revised: 13 February 2019 / Accepted: 14 February 2019 / Published: 19 February 2019
(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)
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Abstract

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side effect—from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein–side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. Results: We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by [email protected]@20. In addition, we confirmed that the paths are novel hypotheses that are worth investigating further. Discussion: The risk of side effects has been an important issue for the US Food and Drug Administration (FDA). However, the causes and mechanisms of such side effects have not been fully elucidated. This study extends previous research on understanding drug side effects by using various techniques such as Named Entity Recognition (NER), Relation Extraction (RE), and semantic similarity. Conclusion: It is not easy to reveal the biomedical mechanisms of side effects due to a huge number of possible paths. However, we automatically generated predictable paths using the proposed approach, which could provide meaningful information to biomedical researchers to generate plausible hypotheses for the understanding of such mechanisms. View Full-Text
Keywords: Biomedical Text Mining; semantic relatedness; Inference of Drug-Protein-Side Effect Relation Biomedical Text Mining; semantic relatedness; Inference of Drug-Protein-Side Effect Relation
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Song, M.; Baek, S.H.; Heo, G.E.; Lee, J.-H. Inferring Drug-Protein–Side Effect Relationships from Biomedical Text. Genes 2019, 10, 159.

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