Improving Literature-Based Discovery with Advanced Text Mining

Korhonen, Anna; Guo, Yufan; Baker, Simon; Yetisgen-Yildiz, Meliha; Stenius, Ulla; Narita, Masashi; Lió, Píetro

doi:10.1007/978-3-319-24462-4_8

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…To overcome the first limitation, it is important to perform much deeper analysis of language (Korhonen et al, 2014) to verify whether the co-occurrence imply a true association which can be considered as an interesting future direction. Additionally, some studies have attempted to utilise evidences from curated databases (e.g., CTD, StringDB) as an alternative for co-occurrence in time-sliced evaluation.…”

Section: Precisionmentioning

confidence: 99%

A systematic review on literature-based discovery workflow

Thilakaratne

Falkner

Atapattu

2019

PeerJ Computer Science

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As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to theinputcomponent, we discuss the data types and data sources used in the literature. Theprocesscomponent presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, theoutputcomponent focuses on the visualisation techniques used in LBD discipline. As for theevaluationcomponent, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions.

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Section: Precisionmentioning

confidence: 99%

A systematic review on literature-based discovery workflow

Thilakaratne

Falkner

Atapattu

2019

PeerJ Computer Science

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“…PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. It provides now access to more than 26 million citations, adding thousands of records daily [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

SemaTyP: a knowledge graph based literature mining method for drug discovery

Sang

Yang

Wang³

et al. 2018

BMC Bioinformatics

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BackgroundDrug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries.MethodsHere, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies’ existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases.ResultsThe experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs.ConclusionsIn this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2167-5) contains supplementary material, which is available to authorized users.

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“…PubMed comprises citations for biomedical literature from MEDLINE and life science journals. Currently, PubMed contains over 26 million biomedical abstracts, which represents an enormous corpus that could be used for drug discovery [3]. The knowledge graph used in this work was constructed with the relations extracted from the PubMed abstracts.…”

Section: Related Materials a Database 1) Pubmed Databasementioning

confidence: 99%

“…It is a lengthy and expensive process, which is estimated to take 14 years and cost approximately $1.8 billion for developing one drug [2]. In contrast, literature based discovery (LBD) is a much less time-consuming and expensive approach to identify new drugs for indications [3]. It has been successfully applied in the field of biomedicine [4].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Hristovski et al [14] introduced discovery patterns which serve as an effective filtering method that reduces the number of false positive discoveries and also supports explanation of discoveries. Although semantic models increase the precision of linking, the major limitation of above semantic models is that more complex associations will go undetected due to semantic models are restricted to the ABC paradigm [3]. More recently, a series of literature mining methods have utilized knowledge graph to discover complex associations.…”

Section: Introductionmentioning

confidence: 99%

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GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

et al. 2019

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Drug discovery is the process by which new candidate medications are discovered. Developing a new drug is a lengthy, complex, and expensive process. Here, in this paper, we propose a biomedical knowledge graph embedding-based recurrent neural network method called GrEDeL, which discovers potential drugs for diseases by mining published biomedical literature. GrEDeL first builds a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the graph data are converted into a low dimensional space by leveraging the knowledge graph embedding methods. After that, a recurrent neural network model is trained by the known drug therapies which are represented by graph embeddings. Finally, it uses the learned model to discover candidate drugs for diseases of interest from biomedical literature. The experimental results show that our method could not only effectively discover new drugs by mining literature, but also could provide the corresponding mechanism of actions for the candidate drugs. It could be a supplementary method for the current traditional drug discovery methods. INDEX TERMS Drug discovery, biomedical knowledge graph, recurrent neural network, deep learning.

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Improving Literature-Based Discovery with Advanced Text Mining

Cited by 9 publications

References 19 publications

A systematic review on literature-based discovery workflow

A systematic review on literature-based discovery workflow

SemaTyP: a knowledge graph based literature mining method for drug discovery

GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

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