A realistic assessment of methods for extracting gene/protein interactions from free text

Kabiljo, Renata; Clegg, Andrew B.; Shepherd, Adrian J.

doi:10.1186/1471-2105-10-233

Cited by 53 publications

(49 citation statements)

References 23 publications

(35 reference statements)

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“…In RelEx, the lemmatization of 175 words is presented, and all the words are captured except for ''ligand'' and ''use'' when lemmatizing the word in our ontology, where ''ligand'' as a noun without corresponding verb and ''use'' as stop words are ignored. The words used in Temkin (Temkin and Gilder, 2003) and Kabiljo (Kabiljo et al, 2009) are all covered by our ontology. As the discussed hierarchy in WordNet, the meaning of the lower-level word in the relation ontology is more general than that of the corresponding higher-level one, i.e., the lower the level where the …”

Section: Coverage Evaluationmentioning

confidence: 99%

“…We compare our relation ontology with the protein interaction relation words that are extracted from corpora BioInfer (Pyysalo et al, 2007), BioCreAtIvE-PPI , LLL05 , Hakenberg (Hakenberg et al, 2006), RelEx (Fundel et al, 2007), Temkin (Temkin and Gilder, 2003), and Kabiljo (Kabiljo et al, 2009), in which the singular and plural of verb and noun are ignored. As in Table 4, the columns Extracted relation words, Ignored, and Recall represent the total number of extracted relation words, the number of omitted words by our method, and the recall of our ontology that is computed in Formula 3, respectively.…”

Section: Coverage Evaluationmentioning

confidence: 99%

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Interaction Relation Ontology Learning

Wang

Zhang

et al. 2014

Journal of Computational Biology

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Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary. Five attributes are populated automatically for each word in interaction relation ontology. As a result, the interaction relation ontology is constructed; it contains a total of 963 words and covers the most relation words used in existing methods of proteins interaction relation.

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Section: Coverage Evaluationmentioning

confidence: 99%

Section: Coverage Evaluationmentioning

confidence: 99%

Interaction Relation Ontology Learning

Wang

Zhang

et al. 2014

Journal of Computational Biology

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“…Such sentences could be those containing biological-specific names such as drug, gene and/or protein names, or biological processes such as protein-to-protein interaction and DNA evolution. The idea of identifying specific words or entity in texts is still an active research area [19,20,21] because of the diversity in biomedical text as discussed in Section 1.1. To our best knowledge, there has not been much work that has addressed boosting sentences and words as part of a ranking strategy.…”

Section: Sentence and Term-based Boostingmentioning

confidence: 99%

Supporting BioMedical Information Retrieval: The BioTracer Approach

Ramampiaro

2011

Transactions on Large-Scale Data- And Knowledge-Centered Systems IV

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Abstract. The large amount and diversity of available biomedical information has put a high demand on existing search systems. Such a tool should be able to not only retrieve the sought information, but also filter out irrelevant documents, while giving the relevant ones the highest ranking. Focusing on biomedical information, this work investigates how to improve the ability for a system to find and rank relevant documents. To achieve this goal, we apply a series of information retrieval techniques to search in biomedical information and combine them in an optimal manner. These techniques include extending and using well-established information retrieval (IR) similarity models such as the Vector Space Model (VSM) and BM25 and their underlying scoring schemes. The techniques also allow users to affect the ranking according to their view of relevance. The techniques have been implemented and tested in a proofof-concept prototype called BioTracer, which extends a Java-based open source search engine library. The results from our experiments using the TREC 2004 Genomic Track collection are promising. Our investigation have also revealed that involving the user in the search process will indeed have positive effects on the ranking of search results, and that the approaches used in BioTracer can be used to meet the user's information needs.Keywords: Biomedical Information Retrieval, Evaluation, BioTracer. Background and MotivationThe continuous increase in the amount of available biomedical information has resulted in a higher demand on biomedical information retrieval (IR) systems. While their use has helped researchers in the field to stay updated on recent literature, many of the existing search systems tend to be either too restrictive (returning results with a low recall) or too broad (finding results with a This article is a revised and an extended version of the ITBAM 2010 paper [1].

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“…We also conduct relation extraction on general named entities, such as "smoking" or "sleep quality". Kabiljo et al (2009) compared pattern-matching techniques against a baseline regular expression approach for gene/protein entity extraction. But existing tools for relation extraction are not as comprehensive as entity recognition tools.…”

Section: Related Workmentioning

confidence: 99%

An Insight Extraction System on BioMedical Literature with Deep Neural Networks

Ganjam

Jain

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.

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A realistic assessment of methods for extracting gene/protein interactions from free text

Cited by 53 publications

References 23 publications

Interaction Relation Ontology Learning

Interaction Relation Ontology Learning

Supporting BioMedical Information Retrieval: The BioTracer Approach

An Insight Extraction System on BioMedical Literature with Deep Neural Networks

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