tmVar: a text mining approach for extracting sequence variants in biomedical literature

Wei, Chih Hsuan; Harris, Bethany; Kao, Hung Yu; Lu, Zhiyong

doi:10.1093/bioinformatics/btt156

Cited by 146 publications

(146 citation statements)

References 31 publications

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“…We used tmVar’s tokenization [37] and part of its features in SimConcept development. Like tmVar, our tokenization separates uppercase characters, lowercase characters and digits.…”

Section: Methodsmentioning

confidence: 99%

SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedical Text

Wei

Leaman

2015

IEEE J. Biomed. Health Inform.

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One particular challenge in biomedical named entity recognition (NER) and normalization is the identification and resolution of composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2). Previous NER and normalization studies have either ignored composite mentions, used simple ad-hoc rules, or only handled coordination ellipsis, making a robust approach for handling multi-type composite mentions greatly needed. To this end, we propose a hybrid method integrating a machine-learning model with a pattern identification strategy to identify the individual components of each composite mention. Our method, which we have named SimConcept, is the first to systematically handle many types of composite mentions. The technique achieves high performance in identifying and resolving composite mentions for three key biological entities: genes (90.42% in F-measure), diseases (86.47% in F-measure) and chemicals (86.05% in F-measure). Furthermore, our results show that using our SimConcept method can subsequently improve the performance of gene and disease concept recognition and normalization.

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“…We used tmVar’s tokenization [37] and part of its features in SimConcept development. Like tmVar, our tokenization separates uppercase characters, lowercase characters and digits.…”

Section: Methodsmentioning

confidence: 99%

SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedical Text

Wei

Leaman

2015

IEEE J. Biomed. Health Inform.

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“…14 has been widely used for named entity recognition, including the biomedical domain 15,16 . In our work, we have used a linear chain CRF implementation provided by CRFSuite 17 .…”

Section: Crfmentioning

confidence: 99%

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature

Jimeno-Yepes

MacKinlay

Gunn

et al. 2018

Preprint

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As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).

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“…The Text Mining approach used two well-known publically available text mining tools: PIE the search (Kim, Kwon et al 2012) and tmVar (Wei, Harris et al 2013). PIE 1 the search is a web service that provides an alternate way of querying PubMed for biologists and database curators.…”

Section: Text Mining Based Article Selectionmentioning

confidence: 99%

“…We took two steps to maximize this limited, valuable resource: First, we reviewed annotations readily available from manually curated PPI databases ) and marked the relevant publications that could be used for the purposes of this challenge; next, we expanded the training set using a set of publically available text mining tools (Kim, Kwon et al 2012, Wei, Harris et al 2013) specifically for the retrieval of literature reporting protein interaction and mutation data.…”

Section: Introductionmentioning

confidence: 99%

BioNLP 2017

Cohen¹,

Demner‐Fushman²,

Ananiadou³

et al. 2017

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According to the Association for Computational Linguistics guidelines on special interest groups (SIGs), The function of a SIG is to encourage interest and activity in specific areas within the ACL's field [1]. Is the SIGBioMed special interest group "within the ACL's field"? The titles of this year's papers suggest that it is, in that the current interest in deep learning in its many and varied manifestations is mirrored in those titles. Do those papers cover a specific area? They do, and in doing so, they demonstrate one of the great satisfactions of working in biomedical natural language processing.One of the joys of involvement in the biomedical natural language processing community is seeing the development of research with clinical applications. As examples of such work being presented at BioNLP 2017, we would like to point out the two papers that discuss the application of natural language processing to the diagnosis of neurological disorders. Bhatia et al. [2] describe an approach to using speech processing in the assessment of patients with amyotrophic lateral sclerosis (also known as Lou Gehrig's disease), one of the more horrific motor neuron diseases. Good assessment of amyotrophic lateral sclerosis patients is important for a number of reasons, including the fact that accurate tracking of the inevitable deterioration that is a hallmark of this disease gives patients and their families the possibility of purposeful planning for the attendant disability and death. However, current methodologies for evaluating the status of amyotrophic lateral sclerosis patients necessarily involve expensive equipment and highly trained personnel; when further developed, this methodology could make such evaluation much more, and more frequently, available to ALS patients. The fact that the work reported here involves a speech modality is especially exciting, as speech-related indicators of future ALS can be present long before diagnosis. The paper uses measurements of phonological features of speech and their divergence from a baseline, and demonstrates correlation with physiological measures.Adams et al. [3] describe work on detecting and categorizing word production errors associated with anomia, a particular kind of inability to find words. Screening for anomia is important because anomia is a symptom of stroke, but it is difficult and time-consuming to do, and therefore is not done as often as it should be. Automatic detection of anomia could be a nice enabler of improved care for stroke victims, but it is made difficult due to the subtlety of the phonological and semantic judgments that have to be made when assessing the phenomenon. The paper uses a combination of language modeling and phonologically-based edit distance calculation to approach the task, applying these techniques to data from the AphasiaBank collection of transcribed aphasic and healthy speech.Although we have summarized only these two examples that address neurological disorders, there are several other papers on the use of natural language proc...

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tmVar: a text mining approach for extracting sequence variants in biomedical literature

Abstract: tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar

Cited by 146 publications

References 31 publications

SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedical Text

SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedical Text

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature

BioNLP 2017

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