Evidence map›Paper›PMID 26072505›Full record

ArticleBioinformatics (Oxford, England)2015

Protein (multi-)location prediction: utilizing interdependencies via a generative model.

Ramanuja Simha, Sebastian Briesemeister, Oliver Kohlbacher, Hagit Shatkay

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Hagit Shatkay-Reshef 1965-2022.Bioinformatics advances · 2022
    Article
  5. Protein Subcellular Localization Prediction.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  6. Article
  7. 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

4 authors.

Ramanuja SimhaDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
Sebastian BriesemeisterDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
Oliver KohlbacherDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
Hagit ShatkayDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationProteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein's function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins.

resultsWe introduce a probabilistic generative model for protein localization, and develop a system based on it-which we call MDLoc-that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems. AVAILABILITY AND IMPLEMENTATION: MDLoc is available at: http://www.eecis.udel.edu/∼compbio/mdloc.

Indexed as

Databases, ProteinModels, TheoreticalBayes TheoremHumansProteinsProtein TransportSubcellular FractionsProteins

Identifiers

PMID26072505
PMCPMC4765880

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LicenceCC BY-NC
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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.