AZuRe, a scalable system for automated term disambiguation of gene and protein names

Podowski, Raf M.; Cleary, J.G.; Goncharoff, Nicholas T.; Amoutzias, Grigoris D.; Hayes, William

doi:10.1109/csb.2004.1332454

Cited by 21 publications

(19 citation statements)

References 12 publications

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“…[28] in which a training set is automatically generated for each human gene, a thesaurus-based method [30] in which a reference description based on either its annotations or MEDLINE abstracts is created for each human gene, and a general method [36] in which Entrez Gene is used to create a profile for each gene sense. All these existing methods need a lot of efforts to create either a training set or a profile for each ambiguous gene symbol.…”

Section: Related Abbreviationsmentioning

confidence: 99%

A system for finding biological entities that satisfy certain conditions from texts

Zhou

Meng

2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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Finding biological entities (such as genes or proteins) that satisfy certain conditions from texts is an important and challenging task in biomedical information retrieval and text mining. It is essential for many biomedical applications, such as drug discovery which normally requires collecting existing scientific facts from documents. This paper presents an effective IR system for this task, in which 1) domain knowledge is incorporated to improve retrieval effectiveness; 2) query expansion with related concepts on multiple semantic levels is employed; 3) a gene symbol disambiguation technique is implemented. We evaluated these techniques and examined two different concept-based IR models. Experiments based upon the proposed framework yield significant improvement (22% for automatic and 16.7% for nonautomatic) over the best reported results of passage retrieval in the Genomics track of TREC 2007.

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Section: Related Abbreviationsmentioning

confidence: 99%

A system for finding biological entities that satisfy certain conditions from texts

Zhou

Meng

2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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“…Both stop-word removal and stemming can be seen as methods for reducing the dimensionality of the feature vector. Podowksi et al (2004) used two bag-of-words feature vectors to disambiguate gene names: one to distinguish between gene and NIT meanings, and one to distinguish between different gene meanings of the same gene name. On a set of 6,521 manually annotated documents relating to 46 different genes, they achieved an accuracy of 88.8% (calculated based on table in original article).…”

Section: Feature Vectorsmentioning

confidence: 99%

Word Sense Disambiguation in the Biomedical Domain: An Overview

Schuemie

Kors

Mons

2005

Journal of Computational Biology

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There is a trend towards automatic analysis of large amounts of literature in the biomedical domain. However, this can be effective only if the ambiguity in natural language is resolved. In this paper, the current state of research in word sense disambiguation (WSD) is reviewed. Several methods for WSD have already been proposed, but many systems have been tested only on evaluation sets of limited size. There are currently only very few applications of WSD in the biomedical domain. The current direction of research points towards statistically based algorithms that use existing curated data and can be applied to large sets of biomedical literature. There is a need for manually tagged evaluation sets to test WSD algorithms in the biomedical domain. WSD algorithms should preferably be able to take into account both known and unknown senses of a word. Without WSD, automatic metaanalysis of large corpora of text will be error prone.

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“…When the latter were included, 233% additional "gene" instances were retrieved, most of which were false positives. In several other studies [17-19], it was also suggested that solving this ambiguity problem is an important requirement for large-scale application of text-mining tools in the biomedical field.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Podowski et al [19] used Bayesian classifier models to disambiguate gene symbols found in LocusLink [28]. Interestingly, their system can distinguish between gene and non-gene meanings of a symbol, acknowledging the fact that many gene symbols are abbreviations of terms with non-gene meanings.…”

Section: Introductionmentioning

confidence: 99%

Thesaurus-based disambiguation of gene symbols

et al. 2005

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BackgroundMassive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck.ResultsWe developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. The thesaurus comprises the information from five human genetic databases and MeSH. The extent of the homonym problem for human gene symbols is shown to be substantial (33% of the genes in our combined thesaurus had one or more ambiguous symbols), not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. A test set of 52,529 Medline abstracts, containing 690 ambiguous human gene symbols taken from OMIM, was automatically generated. Overall accuracy of the disambiguation algorithm was up to 92.7% on the test set.ConclusionThe ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. The proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. The algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications.

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AZuRe, a scalable system for automated term disambiguation of gene and protein names

Cited by 21 publications

References 12 publications

A system for finding biological entities that satisfy certain conditions from texts

A system for finding biological entities that satisfy certain conditions from texts

Word Sense Disambiguation in the Biomedical Domain: An Overview

Thesaurus-based disambiguation of gene symbols

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