BioC interoperability track overview

Comeau, Donald C.; Batista-Navarro, Riza Theresa; Dai, Hong Jie; Doğan, Rezarta Islamaj; Yepes, Antonio Jimeno; Khare, Ritu; Lu, Zhiyong; Marques, Hernani; Mattingly, Carolyn J.; Neves, Mariana; Peng, Yifan; Rak, Rafał; Rinaldi, Fabio; Tsai, Richard Tzong-Han; Verspoor, Karin; Wiegers, Thomas C.; Wu, Cathy H.; Wilbur, W. John

doi:10.1093/database/bau053

Cited by 15 publications

(10 citation statements)

References 39 publications

(80 reference statements)

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“…BioC is a format for sharing text data and annotations and a minimalistic approach to interoperability for biomedical text mining (11). The first BioC task in BioCreative IV (12) released the BioC libraries and called for development of other tools and data resources that used BioC to facilitate interoperability. The BioC task in BioCreative V was designed to make maximal use of BioC to promote data sharing and ease of use and reuse of software created.…”

Section: Introductionmentioning

confidence: 99%

The BioC-BioGRID corpus: full text articles annotated for curation of protein–protein and genetic interactions

et al. 2017

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A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html

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Section: Introductionmentioning

confidence: 99%

The BioC-BioGRID corpus: full text articles annotated for curation of protein–protein and genetic interactions

et al. 2017

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“…This use case illustrates how the BioC interoperability can be applied for fast development of prototypic text mining applications with PubMedPortable in terms of software modularity. More BioC-related software and text corpora can be found in the overview of the BioCreative IV interoperability track [ 36 ] and within the BioC track of the BioCreative V challenge [ 37 ]. Wei et al .…”

Section: Resultsmentioning

confidence: 99%

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications

et al. 2016

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Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user’s system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.

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“…Format incompatibilities can be addressed by creating a standard data format. The recent BioC project is such an example, which has created an interoperable data format that is both straightforward and sufficiently expressive to represent a wide variety of text-mining tasks (43, 44). Another solution may be web services, which hides all configuration and deployment details from the user by providing an API that can be accessed over the Internet, requiring no system installation or maintenance (45, 46).…”

Section: Challenges and Opportunities In Text Miningmentioning

confidence: 99%

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

Singhal¹,

Leaman²,

Catlett³

et al. 2016

Database

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Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.

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BioC interoperability track overview

Cited by 15 publications

References 39 publications

The BioC-BioGRID corpus: full text articles annotated for curation of protein–protein and genetic interactions

The BioC-BioGRID corpus: full text articles annotated for curation of protein–protein and genetic interactions

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

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