Evidence map›Paper›PMID 27597968›Full record

ArticleBioMed research international2016

Identification of Secretory Proteins in Mycobacterium tuberculosis Using Pseudo Amino Acid Composition.

Huan Yang, Hua Tang, Xin-Xin Chen, Chang-Jian Zhang, Pan-Pan Zhu, Hui Ding, Wei Chen, Hao Lin

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
16.0field-weighted citation impact, top 1% of its field
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

46 citing papers in PubMed, 127 citations in OpenAlex.

  1. Review
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  5. iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.Computational and mathematical methods in medicine · 2021
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  7. Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features.Frontiers in bioengineering and biotechnology · 2020
    Article
  8. Article
  9. Review
  10. Article
  11. A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Application of Machine Learning in Microbiology.Frontiers in microbiology · 2019
    Review
  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

8 authors at 3 institutions in 1 country.

Huan YangKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Hua TangDepartment of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
Xin-Xin ChenKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Chang-Jian ZhangKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Pan-Pan ZhuKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
Hui DingKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Wei ChenKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.ORCID 0000-0002-6857-7696
Hao LinKey Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0000-0001-6265-2862
University of Electronic Science and Technology of China · CNNorth China University of Science and Technology · CNSouthwest Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis is killing millions of lives every year and on the blacklist of the most appalling public health problems. Recent findings suggest that secretory protein of Mycobacterium tuberculosis may serve the purpose of developing specific vaccines and drugs due to their antigenicity. Responding to global infectious disease, we focused on the identification of secretory proteins in Mycobacterium tuberculosis. A novel method called MycoSec was designed by incorporating g-gap dipeptide compositions into pseudo amino acid composition. Analysis of variance-based technique was applied in the process of feature selection and a total of 374 optimal features were obtained and used for constructing the final predicting model. In the jackknife test, MycoSec yielded a good performance with the area under the receiver operating characteristic curve of 0.93, demonstrating that the proposed system is powerful and robust. For user's convenience, the web server MycoSec was established and an obliging manual on how to use it was provided for getting around any trouble unnecessary.

Indexed as

Bacterial ProteinsDipeptidesMycobacterium tuberculosisBacterial ProteinsDipeptides

Identifiers

PMID27597968
PMCPMC4997101
OpenAlexW2508405377

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.