Evidence map›Paper›PMID 26019177›Full record

ArticleNucleic acids research2015

INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity.

Damiano Piovesan, Manuel Giollo, Emanuela Leonardi, Carlo Ferrari, Silvio C E Tosatto

Abstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
  2. Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021
    Review
  13. Article
  14. Article
  15. Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis.International journal of molecular sciences · 2020
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

5 authors.

Damiano PiovesanDepartment of Biomedical Sciences, University of Padua, Padua 35121, Italy.
Manuel GiolloDepartment of Biomedical Sciences, University of Padua, Padua 35121, Italy Department of Information Engineering, University of Padua, Padua 35121, Italy.
Emanuela LeonardiDepartment of Women's and Children's Health, University of Padua, Padua 35128, Italy.
Carlo FerrariDepartment of Information Engineering, University of Padua, Padua 35121, Italy.
Silvio C E TosattoDepartment of Biomedical Sciences, University of Padua, Padua 35121, Italy CNR Institute of Neuroscience, Padua 35121, Italy silvio.tosatto@unipd.it.ORCID http://orcid.org/0000-0003-4525-7793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga.

Indexed as

Protein Interaction MappingProtein Structure, TertiarySequence Homology, Amino AcidSoftwareGene OntologyHumansInternetMolecular Sequence AnnotationProteinsProteins

Identifiers

PMID26019177
PMCPMC4489281

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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.