MET network in PubMed: a text-mined network visualization and curation system

Dai, Hong-Jie; Su, Chu Hsien; Lai, Po Ting; Huang, Ming; Jonnagaddala, Jitendra; Jue, Toni Rose; Rao, Shruti; Chou, Hui Jou; Milacic, Marija; Singh, Onkar; Syed-Abdul, Shabbir; Hsu, Wen Lian

doi:10.1093/database/baw090

Cited by 7 publications

(6 citation statements)

References 16 publications

(9 reference statements)

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“…EXTRACT ( 17 ) and Ontogene ( 18 )], others also captured relations between the bioentities [e.g. Argo ( 19 , 20 ), MetastasisWay ( 21 ), egas ( 22 , 23 )] and the most complex one included, in addition, the representation of the extracted information in structure language (e.g. BELIEF).…”

Section: Resultsmentioning

confidence: 99%

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Overview of the interactive task in BioCreative V

et al. 2016

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Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These systems are not meant to replace biocurators, but instead to assist them in one or more literature curation steps. To do so, the user interface is an important aspect that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a track designed for exploring user-system interactions, promoting development of useful TM tools, and providing a communication channel between the biocuration and the TM communities. In BioCreative V, the IAT track followed a format similar to previous interactive tracks, where the utility and usability of TM tools, as well as the generation of use cases, have been the focal points. The proposed curation tasks are user-centric and formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators participated. Two levels of user participation were offered to broaden curator involvement and obtain more feedback on usability aspects. The full level participation involved training on the system, curation of a set of documents with and without TM assistance, tracking of time-on-task, and completion of a user survey. The partial level participation was designed to focus on usability aspects of the interface and not the performance per se. In this case, biocurators navigated the system by performing pre-designed tasks and then were asked whether they were able to achieve the task and the level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested.Database URL: http://www.biocreative.org

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Section: Resultsmentioning

confidence: 99%

“…Argo ( 19 , 20 )]; management systems for curation, where a user can monitor and compare the different annotations [e.g., egas ( 22 , 23 ) and BELIEF], plug-ins/bookmarklets for the web browser [e.g. EXTRACT and MetastasisWay ( 21 )] and network visualization [e.g. GenDisFinder ( 24 ) and MetastasisWay].…”

Section: Resultsmentioning

confidence: 99%

Overview of the interactive task in BioCreative V

et al. 2016

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“…SPBA was first used to solve tasks in general domains such as sentiment classification of Chinese news [10] and answering restaurant-related questions [11]. SPBA has been adapted for biomedical tasks, including miRNA recognition [12], miRNA-target interaction extraction [13], and gene-metastasis relation extraction [14].…”

Section: Related Workmentioning

confidence: 99%

Statistical principle-based approach for gene and protein related object recognition

et al. 2018

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The large number of chemical and pharmaceutical patents has attracted researchers doing biomedical text mining to extract valuable information such as chemicals, genes and gene products. To facilitate gene and gene product annotations in patents, BioCreative V.5 organized a gene- and protein-related object (GPRO) recognition task, in which participants were assigned to identify GPRO mentions and determine whether they could be linked to their unique biological database records. In this paper, we describe the system constructed for this task. Our system is based on two different NER approaches: the statistical-principle-based approach (SPBA) and conditional random fields (CRF). Therefore, we call our system SPBA-CRF. SPBA is an interpretable machine-learning framework for gene mention recognition. The predictions of SPBA are used as features for our CRF-based GPRO recognizer. The recognizer was developed for identifying chemical mentions in patents, and we adapted it for GPRO recognition. In the BioCreative V.5 GPRO recognition task, SPBA-CRF obtained an F-score of 73.73% on the evaluation metric of GPRO type 1 and an F-score of 78.66% on the evaluation metric of combining GPRO types 1 and 2. Our results show that SPBA trained on an external NER dataset can perform reasonably well on the partial match evaluation metric. Furthermore, SPBA can significantly improve performance of the CRF-based recognizer trained on the GPRO dataset.

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“…In terms of functionality, Argo is similar to some of the other systems which were also showcased in the IAT track of BioCreative V. Like Argo, the following systems demonstrated their capabilities for automatic concept annotation and relation extraction: Egas ( 19 ), GenDisFinder ( 20 ), MetastasisWay ( 21 ) and BELIEF ( 22 ). It is worth noting that Egas is the most similar system to Argo.…”

Section: Related Workmentioning

confidence: 99%

Argo: enabling the development of bespoke workflows and services for disease annotation

2016

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Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest.With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources.This article presents the application of Argo’s capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V’s User Interactive Track (IAT), we demonstrated and evaluated Argo’s suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track’s top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo’s support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo’s potential as an enabling technology for curating disease and phenotypic information from literature.Database URL: http://argo.nactem.ac.uk

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MET network in PubMed: a text-mined network visualization and curation system

Cited by 7 publications

References 16 publications

Overview of the interactive task in BioCreative V

Overview of the interactive task in BioCreative V

Statistical principle-based approach for gene and protein related object recognition

Argo: enabling the development of bespoke workflows and services for disease annotation

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