Overview of BioCreAtIvE task 1B: normalized gene lists

Hirschman, Lynette; Colosimo, Marc E.; Morgan, Alexander A.; Yeh, Alexander

doi:10.1186/1471-2105-6-s1-s11

Cited by 134 publications

(115 citation statements)

References 8 publications

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“…We evaluate the effectiveness of the proposed method by using an abstract set of the GENIA corpus 23) and abstract sets about "mouse" and "fly" in the corpora of BioCreAtIvE1 Task 1B 24) . Several hundreds of abstracts are extracted from a set of "GENIA" and they are treated as input documents, namely 'New Documents' in Fig.…”

Section: Discussionmentioning

confidence: 99%

Selection of Effective Sentences from a Corpus to Improve the Accuracy of Identification of Protein Names

Miyanishi

Ozaki

Ohkawa

2009

IPSJ Transactions on Bioinformatics

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As the number of documents about protein structural analysis increases, a method of automatically identifying protein names in them is required. However, the accuracy of identification is not high if the training data set is not large enough. We consider a method to extend a training data set based on machine learning using an available corpus. Such a corpus usually consists of documents about a certain kind of organism species, and documents about different kinds of organism species tend to have different vocabularies. Therefore, depending on the target document or corpus, it is not effective for the accurate identification to simply use a corpus as a training data set. In order to improve the accuracy, we propose a method to select sentences that have a positive effect on identification and to extend the training data set with the selected sentences. In the proposed method, a portion of a set of tagged sentences is used as a validation set. The process to select sentences is iterated using the result of the identification of protein names in a validation set as feedback. In the experiment, compared with the baseline, a method without a corpus, with a whole corpus, or with a part of a corpus chosen at random, the accuracy of the proposed method was higher than any baseline method. Thus, it was confirmed that the proposed method selected effective sentences.

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Section: Discussionmentioning

confidence: 99%

Selection of Effective Sentences from a Corpus to Improve the Accuracy of Identification of Protein Names

Miyanishi

Ozaki

Ohkawa

2009

IPSJ Transactions on Bioinformatics

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“…Most previous work in semantic annotation in the biomedical domain has been restricted to the identification of protein and gene names [21,55]. Recently, the focus has shifted from individual genes and proteins to the identification of entire biological systems, disease names, etc.…”

Section: Biomedical Semantic Annotation Toolsmentioning

confidence: 99%

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

Nebot

Berlanga

2012

Knowl Inf Syst

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Abstract.The increasing amount of unstructured text published on the Web is demanding new tools and methods to automatically process and extract relevant information. Traditional information extraction has focused on harvesting domain-specific, pre-specified relations, which usually requires manual labor and heavy machinery. Especially in the biomedical domain the main efforts have been directed towards the recognition of welldefined entities such as genes or proteins, which constitutes the basis for extracting the relations between the recognized entities. The intrinsic features and scale of the Web demand new approaches able to cope with the diversity of documents, where the number of relations is unbounded and not known in advance. This paper presents a scalable method for the extraction of domain-independent relations from text that exploits the knowledge in the semantic annotations. The method is not geared to any specific domain (e.g., protein-protein interactions, drug-drug interactions, etc.) and does not require any manual input or deep processing. Moreover, the method uses the extracted relations to compute groups of abstract semantic relations characterized by their signature types and synonymous relation strings. This constitutes a valuable source of knowledge when constructing formal knowledge bases, as we enable seamless integration of the extracted relations with the available knowledge resources through the process of semantic annotation. The proposed approach has successfully been applied to a large text collection in the biomedical domain and the results are very encouraging.

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“…[20]). It is especially challenging for applications like this one, since gene names have notoriously low coverage in many publicly available resources and exhibit considerable variability, both in text [10] and in databases [4,6]. In the work reported here, we utilized the Google spell-checking API d .…”

Section: Finding Generifs With Spelling Errorsmentioning

confidence: 99%

GeneRIF QUALITY ASSURANCE AS SUMMARY REVISION

Cohen

Hunter³

2006

Biocomputing 2007

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Like the primary scientific literature, GeneRIFs exhibit both growth and obsolescence. NLM's control over the contents of the Entrez Gene database provides a mechanism for dealing with obsolete data: GeneRIFs are removed from the database when they are found to be of low quality. However, the rapid and extensive growth of Entrez Gene makes manual location of low-quality GeneRIFs problematic. This paper presents a system that takes advantage of the summary-like quality of GeneRIFs to detect low-quality GeneRIFs via a summary revision approach, achieving precision of 89% and recall of 77%. Aspects of the system have been adopted by NLM as a quality assurance mechanism.

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Overview of BioCreAtIvE task 1B: normalized gene lists

Cited by 134 publications

References 8 publications

Selection of Effective Sentences from a Corpus to Improve the Accuracy of Identification of Protein Names

Selection of Effective Sentences from a Corpus to Improve the Accuracy of Identification of Protein Names

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

GeneRIF QUALITY ASSURANCE AS SUMMARY REVISION

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