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chevron_left Multiple amino acid sequence alignment Contact map prediction Solvent accessibility prediction chevron_right
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NeBcon specifications

Information


Unique identifier OMICS_17547
Name NeBcon
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Java, Perl
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Yang Zhang <>
  • person_outline Yang Zhang <>
  • person_outline Yang Zhang <>

Publication for NeBcon

NeBcon in publication

PMCID: 5713875
PMID: 29199978
DOI: 10.1107/S2059798317016436

[…] approaches, each of which trains a statistical model using sequence-derived features such as residue position along the transmembrane helix or sequence separation of residues. most recently, nebcon was proposed to combine multiple contact predictors from both coevolution and machine-learning techniques through naïve bayes classifier and neural network training, which shows an advantage […]

NeBcon institution(s)
Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
NeBcon funding source(s)
The work was supported in part by the National Institute of General Medical Sciences (GM083107, GM116960), the National Science Foundation (DBI1564756), the Natural Science Foundation of China (31628003, 91227115).

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