Automated extraction and semantic analysis of mutation impacts from the biomedical literature

Naderi, Nona; Witte, René

doi:10.1186/1471-2164-13-s4-s10

Cited by 31 publications

(40 citation statements)

References 27 publications

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“…The Open Mutation Miner 8 system extracts mutation information from full text publications and identifies mutation impact information including protein properties such as kinetic and stability data. The system also aims to capture protein function impacts, through detection of Gene Ontology 9 molecular function terms via dictionary look-up with some morphological processing.…”

Section: Related Workmentioning

confidence: 99%

“…A rule-based strategy is employed for association (grounding) of an impact to a mutation. We refer the reader to their work 8 for a thorough review of other text mining systems that focus on extracting mutation information. The scope of these systems is different than our work, as we are interested in detecting all specific protein residue mentions, not only mutation sites, and we focus on catalytic and ligand binding sites.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Protein Catalytic Sites in the Biomedical Literature

et al. 2012

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This paper explores the application of text mining to the problem of detecting protein functional sites in the biomedical literature, and specifically considers the task of identifying catalytic sites in that literature. We provide strong evidence for the need for text mining techniques that address residue-level protein function annotation through an analysis of two corpora in terms of their coverage of curated data sources. We also explore the viability of building a text-based classifier for identifying protein functional sites, identifying the low coverage of curated data sources and the potential ambiguity of information about protein functional sites as challenges that must be addressed. Nevertheless we produce a simple classifier that achieves a reasonable ∼69% F-score on our full text silver corpus on the first attempt to address this classification task. The work has application in computational prediction of the functional significance of protein sites as well as in curation workflows for databases that capture this information.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detection of Protein Catalytic Sites in the Biomedical Literature

et al. 2012

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“…Open issues include the following:

Exploration of alternative mutation extraction and gene normalization tools. For example, a recent paper by Jimeno Yepes and Verspoor (33) compared open source tools for automatic extraction of mutations, including (20, 34–38). Their results suggest that a combination of tools could yield higher performance, which could lead to improvements in both precision and recall.

The adaptation of the current interface for use by expert curators, including explicit representation of concept level output;

The cost of expert review to validate entries before archiving, with a workflow optimized for recall;

The cost of capturing more complex relations, e.g.

…”

Section: Discussionmentioning

confidence: 99%

Hybrid curation of gene–mutation relations combining automated extraction and crowdsourcing

et al. 2014

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Background: This article describes capture of biological information using a hybrid approach that combines natural language processing to extract biological entities and crowdsourcing with annotators recruited via Amazon Mechanical Turk to judge correctness of candidate biological relations. These techniques were applied to extract gene– mutation relations from biomedical abstracts with the goal of supporting production scale capture of gene–mutation–disease findings as an open source resource for personalized medicine. Results: The hybrid system could be configured to provide good performance for gene–mutation extraction (precision ∼82%; recall ∼70% against an expert-generated gold standard) at a cost of $0.76 per abstract. This demonstrates that crowd labor platforms such as Amazon Mechanical Turk can be used to recruit quality annotators, even in an application requiring subject matter expertise; aggregated Turker judgments for gene–mutation relations exceeded 90% accuracy. Over half of the precision errors were due to mismatches against the gold standard hidden from annotator view (e.g. incorrect EntrezGene identifier or incorrect mutation position extracted), or incomplete task instructions (e.g. the need to exclude nonhuman mutations). Conclusions: The hybrid curation model provides a readily scalable cost-effective approach to curation, particularly if coupled with expert human review to filter precision errors. We plan to generalize the framework and make it available as open source software.Database URL: http://www.mitre.org/publications/technical-papers/hybrid-curation-of-gene-mutation-relations-combining-automated

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“…A summary of previous work can be found in Naderi and Witte (2012) 7 . These tools have been shown to achieve a performance over 0.90 in F1 measure, and in some cases almost perfect Precision/Recall, on intrinsic evaluations.…”

Section: Introductionmentioning

confidence: 99%

Mutation extraction tools can be combined for robust recognition of genetic variants in the literature

Jimeno-Yepes

Verspoor

2014

F1000Res

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As the cost of genomic sequencing continues to fall, the amount of data being collected and studied for the purpose of understanding the genetic basis of disease is increasing dramatically. Much of the source information relevant to such efforts is available only from unstructured sources such as the scientific literature, and significant resources are expended in manually curating and structuring the information in the literature. As such, there have been a number of systems developed to target automatic extraction of mutations and other genetic variation from the literature using text mining tools. We have performed a broad survey of the existing publicly available tools for extraction of genetic variants from the scientific literature. We consider not just one tool but a number of different tools, individually and in combination, and apply the tools in two scenarios. First, they are compared in an intrinsic evaluation context, where the tools are tested for their ability to identify specific mentions of genetic variants in a corpus of manually annotated papers, the Variome corpus. Second, they are compared in an extrinsic evaluation context based on our previous study of text mining support for curation of the COSMIC and InSiGHT databases. Our results demonstrate that no single tool covers the full range of genetic variants mentioned in the literature. Rather, several tools have complementary coverage and can be used together effectively. In the intrinsic evaluation on the Variome corpus, the combined performance is above 0.95 in F-measure, while in the extrinsic evaluation the combined recall performance is above 0.71 for COSMIC and above 0.62 for InSiGHT, a substantial improvement over the performance of any individual tool. Based on the analysis of these results, we suggest several directions for the improvement of text mining tools for genetic variant extraction from the literature.

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Automated extraction and semantic analysis of mutation impacts from the biomedical literature

Cited by 31 publications

References 27 publications

Detection of Protein Catalytic Sites in the Biomedical Literature

Detection of Protein Catalytic Sites in the Biomedical Literature

Hybrid curation of gene–mutation relations combining automated extraction and crowdsourcing

Mutation extraction tools can be combined for robust recognition of genetic variants in the literature

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