Evidence map›Paper›PMID 26385205›Full record

ArticleDatabase : the journal of biological databases and curation2015

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

Chao Pang, Annet Sollie, Anna Sijtsma, Dennis Hendriksen, Bart Charbon, Mark de Haan, Tommy de Boer, Fleur Kelpin, Jonathan Jetten, Joeri K van der Velde and 4 more

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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  5. The text2term tool to map free-text descriptions of biomedical terms to ontologies.Database : the journal of biological databases and curation · 2024
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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

14 authors.

Chao PangUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands, University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and.
Annet SollieUniversity of Groningen, University Medical Centre Groningen, Department of Genetics, Groningen, The Netherlands.
Anna SijtsmaUniversity of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and LifeLines Cohort Study and Biobank, Groningen, The Netherlands.
Dennis HendriksenUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Bart CharbonUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Mark de HaanUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Tommy de BoerUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Fleur KelpinUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Jonathan JettenUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Joeri K van der VeldeUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands.
Nynke SmidtUniversity of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and LifeLines Cohort Study and Biobank, Groningen, The Netherlands.
Rolf SijmonsUniversity of Groningen, University Medical Centre Groningen, Department of Genetics, Groningen, The Netherlands.
Hans HillegeUniversity of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and.
Morris A SwertzUniversity of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands, University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and LifeLines Cohort Study and Biobank, Groningen, The Netherlands m.a.swertz@rug.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA.

Indexed as

Biological OntologiesDatabases, FactualKnowledge BasesSoftwareAnimalsData CurationHumans

Identifiers

PMID26385205
PMCPMC4574036

What OpenQuestion holds

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Registered trials

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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.