Evidence map›Paper›PMID 25503992›Full record

ArticleScientific reports2014

A hybrid method for identification of structural domains.

Yongpan Hua, Min Zhu, Yuelong Wang, Zhaoyang Xie, Menglong Li

Abstract read
In one paragraph

Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Yongpan HuaCollege of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, 610064 Chengdu, China.
Min ZhuCollege of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, 610064 Chengdu, China.
Yuelong WangCollege of Chemistry, Sichuan University, No.24 South Section 1, Yihuan Road, 610065 Chengdu, China.
Zhaoyang XieCollege of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, 610064 Chengdu, China.
Menglong LiCollege of Chemistry, Sichuan University, No.24 South Section 1, Yihuan Road, 610065 Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural domains in proteins are the basic units to form various proteins. In the protein's evolution and functioning, domains play important roles. But the definition of domain is not yet precisely given, and the update cycle of structural domain databases is long. The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise. Here, we present a method which recognizes the compact and modular segments of polypeptide chains to identify structural domains, and contrast some data sets to illuminate their effect. The method combines support vector machine (SVM) with K-means algorithm. It is faster and more stable than most current algorithms and performs better. It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties. We have developed a web-server, which would be helpful in identification of structural domains (http://vis.sculab.org/~huayongpan/cgi-bin/domainAssignment.cgi).

Indexed as

AlgorithmsProtein ConformationSupport Vector MachineDatabases, ProteinHumansProteinsProteins

Identifiers

PMID25503992
PMCPMC4265785

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.