Evidence map›Paper›PMID 31119599›Full record

ReviewThe protein journal2019

A Brief History of Protein Sorting Prediction.

Henrik Nielsen, Konstantinos D Tsirigos, Søren Brunak, Gunnar von Heijne

Abstract readReview
In one paragraph

Review in The protein journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers.

0numbers the graph read from it
0cells of the map it votes in
158citing 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

158 citing papers in PubMed.

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  12. Two Novel S-methyltransferases Confer Dimethylsulfide Production in Actinomycetota.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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  15. Identification ofFrontiers in parasitology · 2026
    Article
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  20. JACS Au · 2025
    Article

98 more citing papers are in PubMed but not listed here.

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.

Henrik NielsenDepartment of Health Technology, Section for Bioinformatics, Technical University of Denmark, Kgs. Lyngby, Denmark. henni@dtu.dk.ORCID 0000-0002-9412-9643
Konstantinos D TsirigosDepartment of Health Technology, Section for Bioinformatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
Søren BrunakDepartment of Health Technology, Section for Bioinformatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
Gunnar von HeijneDepartment of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

Indexed as

Protein Sorting SignalsProtein TransportBacteriaComputational BiologyEukaryotaProtein Sorting SignalsBioinformaticsPredictionProtein sortingSignal peptides

Identifiers

PMID31119599
PMCPMC6589146

What OpenQuestion holds

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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.