Biomedical text summarization to support genetic database curation: using Semantic MEDLINE to create a secondary database of genetic information

Workman, T. Elizabeth; Fiszman, Marcelo; Hurdle, John F.; Rindflesch, Thomas C.

doi:10.3163/1536-5050.98.4.003

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…SemRep is a system that extracts biomedical concepts and relations relevant to a given query from the MEDLINE records. Workman, et al [ 61 ] later modified this work to generate domain-specific summaries to support database curation. Workman and Hurdle [ 62 ] applied SemRep to citations obtained from PubMed.…”

Section: Related Workmentioning

confidence: 99%

Figure-Associated Text Summarization and Evaluation

Ramesh

Sethi

2015

PLoS ONE

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Biomedical literature incorporates millions of figures, which are a rich and important knowledge resource for biomedical researchers. Scientists need access to the figures and the knowledge they represent in order to validate research findings and to generate new hypotheses. By themselves, these figures are nearly always incomprehensible to both humans and machines and their associated texts are therefore essential for full comprehension. The associated text of a figure, however, is scattered throughout its full-text article and contains redundant information content. In this paper, we report the continued development and evaluation of several figure summarization systems, the FigSum+ systems, that automatically identify associated texts, remove redundant information, and generate a text summary for every figure in an article. Using a set of 94 annotated figures selected from 19 different journals, we conducted an intrinsic evaluation of FigSum+. We evaluate the performance by precision, recall, F1, and ROUGE scores. The best FigSum+ system is based on an unsupervised method, achieving F1 score of 0.66 and ROUGE-1 score of 0.97. The annotated data is available at figshare.com (http://figshare.com/articles/Figure_Associated_Text_Summarization_and_Evaluation/858903).

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Section: Related Workmentioning

confidence: 99%

Figure-Associated Text Summarization and Evaluation

Ramesh

Sethi

2015

PLoS ONE

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“…Prior to this approach, summarization depended on conventional, static applications, called schemas, which are limited to specified ''subject_predicate_object'' patterns. A different schema was required to summarize for each subheading-type refinement, limiting use to five options: treatment of disease [13], substance interaction [14], diagnosis [15], pharmacogenomics [16], and genetic etiology of disease [17]. Because of its advanced computational methodology, Combo adapts to the properties of each set of SemRep output in determining what is relevant to the user's information need, thus enabling summarization for many subheading concepts.…”

Section: Summarizationmentioning

confidence: 99%

Rethinking information delivery: using a natural language processing application for point-of-care data discovery

Workman¹,

Stoddart²

2012

J Med Libr Assoc

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“…An increasing number of graph-based mining techniques are being applied to characterize the semantic relations in semantic relation extraction tasks [ 15 – 17 ]. In [ 18 ], graph theory and natural language processing techniques are applied to construct a molecular interaction network to extract relationships automatically.…”

Section: Introductionmentioning

confidence: 99%

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

Zhang

et al. 2020

BMC Med Inform Decis Mak

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Background Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue. Methods First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets. Results and conclusions We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

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Biomedical text summarization to support genetic database curation: using Semantic MEDLINE to create a secondary database of genetic information

Abstract: The new summarization schema for genetic etiology has potential as a component in Semantic MEDLINE to support the work of data curators.

Cited by 8 publications

References 18 publications

Figure-Associated Text Summarization and Evaluation

Figure-Associated Text Summarization and Evaluation

Rethinking information delivery: using a natural language processing application for point-of-care data discovery

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

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