Evidence map›Paper›PMID 22151968›Full record

ArticleBMC bioinformatics2011

BioCreative III interactive task: an overview.

Cecilia N Arighi, Phoebe M Roberts, Shashank Agarwal, Sanmitra Bhattacharya, Gianni Cesareni, Andrew Chatr-Aryamontri, Simon Clematide, Pascale Gaudet, Michelle Gwinn Giglio, Ian Harrow and 17 more

Abstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed.

  1. Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII.Database : the journal of biological databases and curation · 2022
    Article
  2. Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID.Database : the journal of biological databases and curation · 2022
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Strategies towards digital and semi-automated curation in RegulonDB.Database : the journal of biological databases and curation · 2017
    Article
  9. Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.Journal of the American Medical Informatics Association : JAMIA · 2016
    Article
  10. Article
  11. BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID.Database : the journal of biological databases and curation · 2016
    Article
  12. Review
  13. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.Database : the journal of biological databases and curation · 2016
    Article
  14. ASAP: a machine learning framework for local protein properties.Database : the journal of biological databases and curation · 2016
    Article
  15. Overview of the interactive task in BioCreative V.Database : the journal of biological databases and curation · 2016
    Article
  16. CD-REST: a system for extracting chemical-induced disease relation in literature.Database : the journal of biological databases and curation · 2016
    Article
  17. Article
  18. Article
  19. Article
  20. Scaling drug indication curation through crowdsourcing.Database : the journal of biological databases and curation · 2015
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Cecilia N ArighiCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu
Phoebe M Roberts
Shashank Agarwal
Sanmitra Bhattacharya
Gianni Cesareni
Andrew Chatr-Aryamontri
Simon Clematide
Pascale Gaudet
Michelle Gwinn Giglio
Ian Harrow
Eva Huala
Martin Krallinger
Ulf Leser
Donghui Li
Feifan Liu
Zhiyong Lu
Lois J Maltais
Naoaki Okazaki
Livia Perfetto
Fabio Rinaldi
Rune Sætre
David Salgado
Padmini Srinivasan
Philippe E Thomas
Luca Toldo
Lynette Hirschman
Cathy H Wu

Funding

Named Entity Recognition and Relationship Extraction in BiomedicineZIALM000002 · NLM · NATIONAL LIBRARY OF MEDICINE · PI LU, ZHIYONG · 2011 to 2012
$1.5M
Linking Text Mining and Data Mining for Biomedical Knowledge DiscoveryG08LM010720 · NLM · UNIVERSITY OF DELAWARE · PI WU, CATHY H. · 2010 to 2012
$438k
NLM NIH HHS 1G08LM10720-01NLM NIH HHS G08 LM010720
6 · The paper itself

Abstract

backgroundThe BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested.

resultsA User Advisory Group (UAG) actively participated in the IAT design and assessment. The task focused on gene normalization (identifying gene mentions in the article and linking these genes to standard database identifiers), gene ranking based on the overall importance of each gene mentioned in the article, and gene-oriented document retrieval (identifying full text papers relevant to a selected gene). Six systems participated and all processed and displayed the same set of articles. The articles were selected based on content known to be problematic for curation, such as ambiguity of gene names, coverage of multiple genes and species, or introduction of a new gene name. Members of the UAG curated three articles for training and assessment purposes, and each member was assigned a system to review. A questionnaire related to the interface usability and task performance (as measured by precision and recall) was answered after systems were used to curate articles. Although the limited number of articles analyzed and users involved in the IAT experiment precluded rigorous quantitative analysis of the results, a qualitative analysis provided valuable insight into some of the problems encountered by users when using the systems. The overall assessment indicates that the system usability features appealed to most users, but the system performance was suboptimal (mainly due to low accuracy in gene normalization). Some of the issues included failure of species identification and gene name ambiguity in the gene normalization task leading to an extensive list of gene identifiers to review, which, in some cases, did not contain the relevant genes. The document retrieval suffered from the same shortfalls. The UAG favored achieving high performance (measured by precision and recall), but strongly recommended the addition of features that facilitate the identification of correct gene and its identifier, such as contextual information to assist in disambiguation. DISCUSSION: The IAT was an informative exercise that advanced the dialog between curators and developers and increased the appreciation of challenges faced by each group. A major conclusion was that the intended users should be actively involved in every phase of software development, and this will be strongly encouraged in future tasks. The IAT Task provides the first steps toward the definition of metrics and functional requirements that are necessary for designing a formal evaluation of interactive curation systems in the BioCreative IV challenge.

Indexed as

GenesAnimalsComputational BiologyData MiningPeriodicals as TopicPlants

Identifiers

PMID22151968
PMCPMC3269939

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.