Combining multiple evidence for gene symbol disambiguation

Xu, Hua; Fan, Jungwei; Friedman, Carol

doi:10.3115/1572392.1572400

Cited by 4 publications

(10 citation statements)

References 27 publications

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“…Several existing works [9], [10], [11] perform local species disambiguation, assigning each gene mention in the document a mention-specific species tag, while Kappeler et al [7] focuses on global species disambiguation for the document as a whole. In Wang and Matthews [11], species detection is tackled with a maximum entropy machine learning model based on document context features, such as the words (or more specifically the nouns or adjectives) to the left or right of an entity mention, and species words and IDs identified in the document.…”

Section: Gene Normalizationmentioning

confidence: 99%

Exploring Species-Based Strategies for Gene Normalization

Verspoor

Roeder

Johnson

et al. 2010

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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We introduce a system developed for the BioCreativeII.5 community evaluation of information extraction of proteins and protein interactions. The paper focuses primarily on the gene normalization task of recognizing protein mentions in text and mapping them to the appropriate database identifiers based on contextual clues. We outline a "fuzzy" dictionary lookup approach to protein mention detection that matches regularized text to similarly-regularized dictionary entries. We describe several different strategies for gene normalization that focus on species or organism mentions in the text, both globally throughout the document and locally in the immediate vicinity of a protein mention, and present the results of experimentation with a series of system variations that explore the effectiveness of the various normalization strategies, as well as the role of external knowledge sources. While our system was neither the best nor the worst performing system in the evaluation, the gene normalization strategies show promise and the system affords the opportunity to explore some of the variables affecting performance on the BCII.5 tasks.

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Section: Gene Normalizationmentioning

confidence: 99%

Exploring Species-Based Strategies for Gene Normalization

Verspoor

Roeder

Johnson

et al. 2010

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…We used two evaluation metrics in our study, namely precision and coverage. They are the standard measures in the document classification community and this allowed us to make a direct comparison between our results and those in [ 14 , 15 ]. As the goal of our first set of approaches was to construct a system with good precision and then extend its results to obtain full coverage, we decided to examine both measures and not apply their aggregation (like the F measure).…”

Section: Resultsmentioning

confidence: 99%

“…It utilises the synonyms of the target gene name which are present in the document of the test gene. In this study we present experimental results on the GSD datasets built by Xu et al [ 14 , 15 ]. In [ 14 ] Xu and his colleagues took the words of the abstracts, the MeSH codes provided along with the MedLine articles, the words of the texts and some computer tagged information (UMLS CUIs and biomedical entities) as features while in [ 15 ] they experimented with the use of combinations of these features.…”

Section: Introductionmentioning

confidence: 99%

“…In this study we present experimental results on the GSD datasets built by Xu et al [ 14 , 15 ]. In [ 14 ] Xu and his colleagues took the words of the abstracts, the MeSH codes provided along with the MedLine articles, the words of the texts and some computer tagged information (UMLS CUIs and biomedical entities) as features while in [ 15 ] they experimented with the use of combinations of these features. They used them to get manually disambiguated instances (training data) and applied a vector space model with cosine similarity measure between the abstracts in question and the gene profiles which were in fact the centroids of the training instances.…”

Section: Introductionmentioning

confidence: 99%

“…The GSD datasets for yeast, fly and mouse are generated using MedLine abstracts and the Entrez 'gene2pubmed' file [ 3 ], which is manually disambiguated [ 14 ]. The dataset for human genes was derived [ 15 ] from the training and evaluation sets of the BioCreative II GN task [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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The strength of co-authorship in gene name disambiguation

Farkas

2008

BMC Bioinformatics

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BackgroundA biomedical entity mention in articles and other free texts is often ambiguous. For example, 13% of the gene names (aliases) might refer to more than one gene. The task of Gene Symbol Disambiguation (GSD) – a special case of Word Sense Disambiguation (WSD) – is to assign a unique gene identifier for all identified gene name aliases in biology-related articles. Supervised and unsupervised machine learning WSD techniques have been applied in the biomedical field with promising results. We examine here the utilisation potential of the fact – one of the special features of biological articles – that the authors of the documents are known through graph-based semi-supervised methods for the GSD task.ResultsOur key hypothesis is that a biologist refers to each particular gene by a fixed gene alias and this holds for the co-authors as well. To make use of the co-authorship information we decided to build the inverse co-author graph on MedLine abstracts. The nodes of the inverse co-author graph are articles and there is an edge between two nodes if and only if the two articles have a mutual author. We introduce here two methods using distances (based on the graph) of abstracts for the GSD task. We found that a disambiguation decision can be made in 85% of cases with an extremely high (99.5%) precision rate just by using information obtained from the inverse co-author graph. We incorporated the co-authorship information into two GSD systems in order to attain full coverage and in experiments our procedure achieved precision of 94.3%, 98.85%, 96.05% and 99.63% on the human, mouse, fly and yeast GSD evaluation sets, respectively.ConclusionBased on the promising results obtained so far we suggest that the co-authorship information and the circumstances of the articles' release (like the title of the journal, the year of publication) can be a crucial building block of any sophisticated similarity measure among biological articles and hence the methods introduced here should be useful for other biomedical natural language processing tasks (like organism or target disease detection) as well.

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