Evidence map›Paper›PMID 27374119›Full record

ArticleDatabase : the journal of biological databases and curation2016

neXtA5: accelerating annotation of articles via automated approaches in neXtProt.

Luc Mottin, Julien Gobeill, Emilie Pasche, Pierre-André Michel, Isabelle Cusin, Pascale Gaudet, Patrick Ruch

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Overview of the BioCreative VI text-mining services for Kinome Curation Track.Database : the journal of biological databases and curation · 2018
    Article
  6. Assisting document triage for human kinome curation via machine learning.Database : the journal of biological databases and curation · 2018
    Article
  7. Triage by ranking to support the curation of protein interactions.Database : the journal of biological databases and curation · 2017
    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

7 authors.

Luc MottinBiTeM Group, University of Applied Sciences, Western Switzerland-HEG Genève, Information Science Department SIB Text Mining, Swiss Institute of Bioinformatics luc.mottin@hesge.ch patrick.ruch@hesge.ch.
Julien GobeillBiTeM Group, University of Applied Sciences, Western Switzerland-HEG Genève, Information Science Department SIB Text Mining, Swiss Institute of Bioinformatics.
Emilie PascheBiTeM Group, University of Applied Sciences, Western Switzerland-HEG Genève, Information Science Department SIB Text Mining, Swiss Institute of Bioinformatics.
Pierre-André MichelCalipho Group, Swiss Institute of Bioinformatics.
Isabelle CusinCalipho Group, Swiss Institute of Bioinformatics.
Pascale GaudetCalipho Group, Swiss Institute of Bioinformatics.
Patrick RuchBiTeM Group, University of Applied Sciences, Western Switzerland-HEG Genève, Information Science Department SIB Text Mining, Swiss Institute of Bioinformatics.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid increase in the number of published articles poses a challenge for curated databases to remain up-to-date. To help the scientific community and database curators deal with this issue, we have developed an application, neXtA5, which prioritizes the literature for specific curation requirements. Our system, neXtA5, is a curation service composed of three main elements. The first component is a named-entity recognition module, which annotates MEDLINE over some predefined axes. This report focuses on three axes: Diseases, the Molecular Function and Biological Process sub-ontologies of the Gene Ontology (GO). The automatic annotations are then stored in a local database, BioMed, for each annotation axis. Additional entities such as species and chemical compounds are also identified. The second component is an existing search engine, which retrieves the most relevant MEDLINE records for any given query. The third component uses the content of BioMed to generate an axis-specific ranking, which takes into account the density of named-entities as stored in the Biomed database. The two ranked lists are ultimately merged using a linear combination, which has been specifically tuned to support the annotation of each axis. The fine-tuning of the coefficients is formally reported for each axis-driven search. Compared with PubMed, which is the system used by most curators, the improvement is the following: +231% for Diseases, +236% for Molecular Functions and +3153% for Biological Process when measuring the precision of the top-returned PMID (P0 or mean reciprocal rank). The current search methods significantly improve the search effectiveness of curators for three important curation axes. Further experiments are being performed to extend the curation types, in particular protein-protein interactions, which require specific relationship extraction capabilities. In parallel, user-friendly interfaces powered with a set of JSON web services are currently being implemented into the neXtProt annotation pipeline.Available on: http://babar.unige.ch:8082/neXtA5Database URL: http://babar.unige.ch:8082/neXtA5/fetcher.jsp.

Indexed as

MEDLINEData CurationData MiningElectronic Data ProcessingSearch Engine

Identifiers

PMID27374119
PMCPMC4930835

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.