Evidence map›Paper›PMID 21805636›Full record

ArticleProteomics2011

Protein 8-class secondary structure prediction using conditional neural fields.

Zhiyong Wang, Feng Zhao, Jian Peng, Jinbo Xu

Abstract read
In one paragraph

Article in Proteomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

  1. Article
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  4. Sculpting conducting nanopore size and shape throughbioRxiv : the preprint server for biology · 2023
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  9. Review
  10. Proteomic Tools for the Analysis of Cytoskeleton Proteins.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  11. Article
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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

4 authors.

Zhiyong WangToyota Technological Institute at Chicago, 6045 S Kenwood, Chicago, IL 60637, USA.
Feng Zhao
Jian Peng
Jinbo Xu

Funding

New Computational Methods for Data-driven Protein Structure PredictionR01GM089753 · NIGMS · TOYOTA TECHNOLOGICAL INSTITUTE / CHICAGO · PI XU, JINBO · 2010 to 2023
$3.8M
NIGMS NIH HHS R01 GM089753NIGMS NIH HHS R01GM089753
6 · The paper itself

Abstract

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA.

Indexed as

AlgorithmsDatabases, ProteinModels, BiologicalNeural Networks, ComputerProteinsProtein Structure, SecondaryProteomicsProteins

Identifiers

PMID21805636
PMCPMC3341732

What OpenQuestion holds

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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.