MBA: a literature mining system for extracting biomedical abbreviations

Xu, Yun; Wang, Zhi-Hao; Lei, Yiming; Zhao, Yuzhong; Xue, Yu

doi:10.1186/1471-2105-10-14

Cited by 20 publications

(18 citation statements)

References 14 publications

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“…Nevertheless, they can complement acronym-type ABRs recognition techniques by further processing the remaining "candidate" ABRs, as e.g. in the MDA system [17].…”

Section: Related Workmentioning

confidence: 99%

Building a Repository for Inferring the Meaning of Abbreviations Used in Clinical Studies

Chondrogiannis¹,

Karanastasis²,

Andronikou³

et al. 2017

JCP

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Abbreviations are widely used in clinical studies. Identifying their meaning is an essential step for further processing of the provided data. In this work, a novel system developed for detecting the meaning and context of the abbreviations specified in the whole corpus of clinicaltrials.gov studies is presented in detail. In the background, innovative algorithms and techniques were used for both acronym and non-acronym type abbreviation recognition purposes, while their evaluation was performed based on an abbreviations annotated corpus of clinical studies, which was purposely developed. The outcome of this work is a repository consisting of approximately 28 thousand abbreviations, while for each of them its possible senses along with their context have been recorded, so that it can be utilized for inferring the meaning of abbreviations met in existing and future documents in this domain. Further analysis of the recorded data indicated, amongst others, that there are considerable differences among abbreviations and their expansions or senses depending on the scientific domain in which they are used. Also, the abbreviations consisting of three and especially two characters are already highly ambiguous and their possible senses are going to be significantly increased in the following years.

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“…Nevertheless, they can complement acronym-type ABRs recognition techniques by further processing the remaining "candidate" ABRs, as e.g. in the MDA system [17].…”

Section: Related Workmentioning

confidence: 99%

Building a Repository for Inferring the Meaning of Abbreviations Used in Clinical Studies

Chondrogiannis¹,

Karanastasis²,

Andronikou³

et al. 2017

JCP

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“…The second type of post-processing in our hybrid approach is the abbreviation resolution (Sohn et al, 2008;Xu et al, 2009). Abbreviations are widely and commonly used in biomedical text (Sohn et al, 2008).…”

Section: Abbreviation Identification Algorithmmentioning

confidence: 99%

“…For example, BANNER left the abbreviation resolution as its future work (Leaman and Gonzalez, 2008). Various approaches have been proposed to automatically identify the abbreviation and its definition in a biomedical abstracts (Xu et al, 2009;Schwartz and Hearst, 2003;Park and Byrd 2001). We introduce a hybrid abbreviation identification algorithm by combining the features of two existing algorithms (Schwartz and Hearst, 2003;Park and Byrd 2001).…”

Section: Abbreviation Identification Algorithmmentioning

confidence: 99%

A hybrid named entity tagger for tagging human proteins/genes

Raja

Subramani

Natarajan

2014

IJDMB

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The predominant step and pre-requisite in the analysis of scientific literature is the extraction of gene/protein names in biomedical texts. Though many taggers are available for this Named Entity Recognition (NER) task, we found none of them achieve a good state-of-art tagging for human genes/proteins. As most of the current text mining research is related to human literature, a good tagger to precisely tag human genes and proteins is highly desirable. In this paper, we propose a new hybrid approach based on (a) machine learning algorithm (conditional random fields), (b) set of (manually constructed) rules, and (c) a novel abbreviation identification algorithm to surmount the common errors observed in available taggers to tag human genes/proteins. Experiment results on JNLPBA2004 corpus show that our domain specific approach achieves a high precision of 80.47, F-score of 75.77 and outperforms most of the state-of-the-art systems. However, the recall of 71.60 still remains low and leaves much room for future improvement.

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“…Also see Wren et al for a review of four methods [67]: ARGH, the Stanford Biomedical Abbreviation Server, AcroMed and SaRAD [67,68,213–216]. A recent paper by Xu et al describes MBA, a system that achieves similarly high performance [69]. It specializes in identifying nonacronym abbreviations such as ‘Fas’ used as an abbreviation for the gene ‘ CD95 ’, using a statistical method in which they count the number of articles that contain both the candidate definition and the abbreviation and then use this in scoring each candidate definition/abbreviation pair.…”

Section: Identification Of Information Within the Documents: Informatmentioning

confidence: 99%

Recent Progress in Automatically Extracting Information from the Pharmacogenomic Literature

2010

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The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports. We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies. Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature. These methods enable extraction of specific types of information and can also provide answers to general, systemic queries. In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications. KeywordsBioNLP; classification; curation; data mining; gene-drug relationships; information extraction; information retrieval; machine learning; natural language processing; NLP; pharmacogenetics; pharmacogenomics; text mining After several decades of pharmacogenomics research, it is clear that the overall pharmacologic effects of medications are typically not monogenic traits, but are determined by the interactions among several genes encoding proteins involved in numerous pathways [1]. Polygenic determinants of drug response are often difficult to elucidate in clinical studies; however, recently functional genomics and high-throughput screening methods have been providing powerful new tools to reveal these interactions. To uncover the relationships between biological systems and drug response, pharmacogenomic researchers must † Author for correspondence: russ.altman@stanford.edu.For reprint orders, please contact: reprints@futuremedicine.com Financial & competing interests disclosureThe authors acknowledge support from NIH LM07033 (Yael Garten), GM61374 (Yael Garten, Adrien Coulet and Russ B Altman), LM05652 (Russ B Altman) and the National Center for Biomedical Computing (NCBC) National Institute of Health roadmap initiative; NIH grant U54 HG004028 (Adrien Coulet). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. NIH Public Access Author ManuscriptPharmacogenomics. Author manuscript; available in PMC 2011 August 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript assimilate knowledge from a multitude of disciplines, on levels ranging from genomic, molecular, cellular, tissue, organ and organismic.Therefore, researchers need the ability to que...

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MBA: a literature mining system for extracting biomedical abbreviations

Cited by 20 publications

References 14 publications

Building a Repository for Inferring the Meaning of Abbreviations Used in Clinical Studies

Building a Repository for Inferring the Meaning of Abbreviations Used in Clinical Studies

A hybrid named entity tagger for tagging human proteins/genes

Recent Progress in Automatically Extracting Information from the Pharmacogenomic Literature

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