Analysis of Protein/Protein Interactions Through Biomedical Literature: Text Mining of Abstracts vs. Text Mining of Full Text Articles

Martin, E; Bremer, E.; Guerin, Marie-Claude; DeSesa, Catherine; Jouve, Olivier

doi:10.1007/978-3-540-30478-4_9

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…One study using a corpus of 3,800 articles with focus on Caenorhabditis elegans noted an increase in recall from 45% to 95% when including the full text [ 21 ]. Other studies have worked with even smaller corpora [ 17 , 22 , 23 ]. One study have even noted that the majority of claims within an article is not reported in the abstract [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

et al. 2018

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Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

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Section: Introductionmentioning

confidence: 99%

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

et al. 2018

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“…A review of the titles and abstracts of these 69 articles indicated that 58 of 69 studies applied text mining as a technique. The review shows that text mining is common in the biomedical field e.g., to identify specific diseases, genes, drugs, and their relationships (Alshuwaier et al, 2017;Libbus & Rindflesch, 2002;Martin et al, 2004;Quan et al, 2014;Singhal et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

A Framework for the Application of Machine Learning in Is Literature Reviews

2022

Proceedings

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“…A review of the titles and abstracts of these 69 articles indicated that 58 of 69 studies applied text mining as a technique. In general, text mining is common in the biomedical field and is often used in PubMed publications to identify specific diseases, genes, drugs, and their relationships (Alshuwaier et al, 2017;Libbus & Rindflesch, 2002;Martin et al, 2004;Quan et al, 2014;Singhal et al, 2016). Figure 1 shows the distribution of the literature.…”

Section: Related Workmentioning

confidence: 99%

The Application of Machine Learning in Literature Reviews: A Framework

2022

IJCISIS

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Literature reviews are essential for any scientific work, both as part of a dissertation or as a stand-alone work. Scientists benefit from the fact that more and more literature is available in electronic form, and finding and accessing relevant literature has become more accessible through scientific databases. However, a traditional literature review method is characterized by a highly manual process, while technologies and methods in big data, machine learning, and text mining have advanced. Especially in areas where research streams are rapidly evolving, and topics are becoming more comprehensive, complex, and heterogeneous, it is challenging to provide a holistic overview and identify research gaps manually. Therefore, we have developed a framework that supports the traditional approach of conducting a literature review using machine learning and text mining methods. The framework is particularly suitable in cases where a large amount of literature is available, and a holistic understanding of the research area is needed. The framework consists of several steps in which the critical mind of the scientist is supported by machine learning. The unstructured text data is transformed into a structured form through data preparation realized with text mining, making it applicable for various machine learning techniques. A concrete example in the field of smart cities makes the framework tangible.

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Analysis of Protein/Protein Interactions Through Biomedical Literature: Text Mining of Abstracts vs. Text Mining of Full Text Articles

Cited by 8 publications

References 17 publications

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

A Framework for the Application of Machine Learning in Is Literature Reviews

The Application of Machine Learning in Literature Reviews: A Framework

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