Playing Biology's Name Game: Identifying Protein Names in Scientific Text

Hanisch, Daniel; Fluck, Juliane; Mevissen, Heinz-Theodor; Zimmer, Ralf

doi:10.1142/9789812776303_0038

Cited by 66 publications

(56 citation statements)

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“…A Nature news feature titled Biology's Name Game (Pearson, 2001) pointed out the difficulties in identifying protein and gene names in biomedical text. This challenging problem is the center of active and fruitful research (Hanisch et al, 2003;Hirschman et al, 2005;Tanabe and Wilbur, 2002), under the assumption that the best way for Biology to utilize the literature is by first accurately identifying biological entities in it. So far, the progress made in biological named entity recognition has not translated into a quantitative improvement with respect to any specific biological problem.…”

Section: Discussion and Outlookmentioning

confidence: 99%

SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data

et al. 2007

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Motivation: Knowing the localization of a protein within the cell helps elucidate its role in biological processes, its function and its potential as a drug target. Thus, subcellular localization prediction is an active research area. Numerous localization prediction systems are described in the literature; some focus on specific localizations or organisms, while others attempt to cover a wide range of localizations. Results: We introduce SherLoc, a new comprehensive system for predicting the localization of eukaryotic proteins. It integrates several types of sequence and text-based features. While applying the widely used support vector machines (SVMs), SherLoc's main novelty lies in the way in which it selects its text sources and features, and integrates those with sequence-based features. We test SherLoc on previously used datasets, as well as on a new set devised specifically to test its predictive power, and show that SherLoc consistently improves on previous reported results. We also report the results of applying SherLoc to a large set of yetunlocalized proteins.

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Section: Discussion and Outlookmentioning

confidence: 99%

SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data

et al. 2007

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“…Table 3 shows the precision, recall and f1 of our system, as evaluated on devtest data and blind test data, 15 together with iaa. Recall that iaa indicates the performance by human experts on the same task.…”

Section: Resultsmentioning

confidence: 99%

“…15 Evaluation on blind test data was carried out independently by a third-party organisation who only evaluated the ti system as a whole. This explains why the performance of species tagging on the blind test data is unknown.…”

Section: Resultsmentioning

confidence: 99%

“…In specific applications such as extraction of protein-protein interactions, identification of protein names is even more important. In addition, protein name identification could be more challenging, as researchers observed that protein names tend to be more ambiguous [15] than genes, because protein names a) are inclined to contain multiple words than gene names and b) their naming convention is more diverse.…”

Section: Related Workmentioning

confidence: 99%

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Rule-Based Protein Term Identification with Help from Automatic Species Tagging

Wang

2007

Computational Linguistics and Intelligent Text Processing

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Abstract. In biomedical articles, terms often refer to different protein entities. For example, an arbitrary occurrence of term p53 might denote thousands of proteins across a number of species. A human annotator is able to resolve this ambiguity relatively easily, by looking at its context and if necessary, by searching an appropriate protein database. However, this phenomenon may cause much trouble to a text mining system, which does not understand human languages and hence can not identify the correct protein that the term refers to. In this paper, we present a Term Identification system which automatically assigns unique identifiers, as found in a protein database, to ambiguous protein mentions in texts. Unlike other solutions described in literature, which only work on gene/protein mentions on a specific model organism, our system is able to tackle protein mentions across many species, by integrating a machine-learning based species tagger. We have compared the performance of our automatic system to that of human annotators, with very promising results.

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“…We name this relation "hasTypographicVariant" and it seems very promising in the conceptual modeling of biomedical domains since the names of many entities are written in a non-standardized name convention nomenclature [34]. For example, Hanisch et al [35] notice this in the gene domain. The identification of different typographic variants of existing instances is performed by a novel compression-based clustering algorithm, named COCLU (COmpression-based CLUstering) [36].…”

Section: Knowledge Refinementmentioning

confidence: 99%