Getting to the (c)ore of knowledge: mining biomedical literature

Bruijn, Berry de; Martin, Joel

doi:10.1016/s1386-5056(02)00050-3

Cited by 106 publications

(40 citation statements)

References 43 publications

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“…Literature mining is a powerful method for elucidating major trends across time in published scientific literature and allows for topic maps to be built [26]. Our primary goal of literature mining is to discover the research trend on this subject.…”

Section: Methodsmentioning

confidence: 99%

A Systematic Literature Mining of Sponge City: What Has Been Done and The Challenges Standing Ahead

Li¹,

Xu²,

Yao³

2018

Preprint

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As the increase threat of flood risk and environmental safety due to the urbanization, Sponge city research has been attracting extensive attention both in practical and theoretical research field. To date, there are only scattered studies about Sponge city. Moreover, vary names of Sponge city prevalent in different countries, which leads to disconnection of literature in the same field of Sponge city. In this paper, a thorough systematic literature mining of Sponge city is presented. A literature analysis system is created, which includes literature export from Web of Sciences and systematic analysis via NoteExpress and CiteSpace. Some literature statistical results are derived. Challenges and opportunities for future research are anticipated. Our goals are to promote this promising thought, summarize past research, and identify issues for future research to create impacts on the practice of Sponge city.

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Section: Methodsmentioning

confidence: 99%

A Systematic Literature Mining of Sponge City: What Has Been Done and The Challenges Standing Ahead

Li¹,

Xu²,

Yao³

2018

Preprint

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“…Named Entity Tagging (NET) is a popular text mining approach which searches through all OMIM records for the terms related to prostate cancer and returns those records containing the terms as candidate prostate-cancer-related genes (de Bruin and Martin, 2002). The NET approach depends on the established human annotations of OMIM and therefore limits its use in finding potential links between phenotypes and genes.…”

Section: Validation With Text Mining Of Omimmentioning

confidence: 99%

Link test—A statistical method for finding prostate cancer biomarkers

Deng

Geng

Bastola

et al. 2006

Computational Biology and Chemistry

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We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ text mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first crossplatform studies of cancer biomarkers.

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“…It was recognized that natural language processing and text data mining is effective for information extraction. Most of this work focuses on extracting information and knowledge from research literature and abstracts [4,5,7,13,17,19,27,34] from such online repositories as Medline and PubMed. In some domains, this extraction is referred to as literature mining or web mining [23].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Towards applying text mining and natural language processing for biomedical ontology acquisition

Inniss

Lee²,

Light

et al. 2006

Proceedings of the 1st International Workshop on Text Mining in Bioinformatics

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The use of text mining and natural language processing can extend into the realm of knowledge acquisition and management for biomedical applications. In this paper, we describe how we implemented natural language processing and text mining techniques on the transcribed verbal descriptions from retinal experts of biomedical disease features. The feature-attribute pairs generated were then incorporated within a user interface for a collaborative ontology development tool. This tool, IDOCS, is being used in the biomedical domain to help retinal specialists reach a consensus on a common ontology for describing age-related macular degeneration (AMD). We compare the use of traditional text mining and natural language processing techniques with that of a retinal specialist's analysis and discuss how we might integrate these techniques for future biomedical ontology and user interface development.

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Getting to the (c)ore of knowledge: mining biomedical literature

Cited by 106 publications

References 43 publications

A Systematic Literature Mining of Sponge City: What Has Been Done and The Challenges Standing Ahead

A Systematic Literature Mining of Sponge City: What Has Been Done and The Challenges Standing Ahead

Link test—A statistical method for finding prostate cancer biomarkers

Towards applying text mining and natural language processing for biomedical ontology acquisition

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