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Citations per year

Number of citations per year for the bioinformatics software tool NNcon
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Tool usage distribution map

This map represents all the scientific publications referring to NNcon per scientific context
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Protocols

NNcon specifications

Information


Unique identifier OMICS_07130
Name NNcon
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes

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No version available

Publication for NNcon

NNcon citations

 (7)
library_books

Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non contacts

2017
BMC Bioinformatics
PMCID: 5576353
PMID: 28851269
DOI: 10.1186/s12859-017-1807-5

[…] Realizing the importance of contact definition at higher distance thresholds, tools like NNcon [] predict contacts at both distance thresholds – 8 Å and 12 Å. There are, however, challenges in predicting contacts at higher distance thresholds and utilizing them to build models. The first […]

library_books

The evolution of logic circuits for the purpose of protein contact map prediction

2017
PeerJ
PMCID: 5398280
PMID: 28439455
DOI: 10.7717/peerj.3139

[…] plmDCA, GREMLIN, and EVfold at short range at L/10, L/5, and L/2. At medium range, Markov networks have comparable performance to PSICOV and EVfold at all L-values, and do roughly as well as plmDCA, NNcon, and GREMLIN at L/2. At long range, Markov networks have difficulty compared to the other methods.For CASP 11, Markov networks again do better than PSICOV, plmDCA, GREMLIN, and EVfold on short-r […]

call_split

Benchmarking Deep Networks for Predicting Residue Specific Quality of Individual Protein Models in CASP11

2016
Sci Rep
PMCID: 4725912
PMID: 26763289
DOI: 10.1038/srep19301
call_split See protocol

[…] this feature;The difference of solvent accessibility between that predicted from sequence by SSPRO and the values from the model as parsed by DSSP. This is similar to the secondary structure feature;NNcon was used to predict the residue-residue contact probabilities based on amino acid sequence. This produced a probability for each pair of residues in the protein which indicated the likelihood of […]

library_books

A new ensemble coevolution system for detecting HIV 1 protein coevolution

2015
Biol Direct
PMCID: 4332441
PMID: 25564011
DOI: 10.1186/s13062-014-0031-8

[…] erage accuracies over the 7 datasets for both the L/2 and L top-ranked predictions (average accuracy = 0.35, 0.27, respectively) (Additional file : Table S4). Comparing CNPR to the second best method NNcon, average accuracies over the 7 datasets for the L/2 and L top-ranked predictions increased by 0.061 (17.6%) and 0.031 (11.5%), respectively (Table , Additional file : Table S4). Thirdly, we meas […]

call_split

MSACompro: protein multiple sequence alignment using predicted secondary structure, solvent accessibility, and residue residue contacts

2011
BMC Bioinformatics
PMCID: 3299741
PMID: 22168237
DOI: 10.1186/1471-2105-12-472
call_split See protocol

[…] xj′ in protein sequence X, and yij′ is the contact probability score between amino acid yi' and yj' in protein sequence Y. The residue-residue contact probabilities are predicted from the sequence by NNcon [] (http://sysbio.rnet.missouri.edu/multicom_toolbox/). The contact map correlation score matrix CMapXY is calculated as the multiplication of CMapX and CMapY: (7) C M a p X Y = C M a […]

library_books

Correlated mutations via regularized multinomial regression

2011
BMC Bioinformatics
PMCID: 3247924
PMID: 22082126
DOI: 10.1186/1471-2105-12-444

[…] act predictor could be boosted by combining the correlated mutation search with such approaches. This could include for example specialized beta-sheet contact prediction algorithms such as applied by NNcon [].The dependence of RMRCM performance on the number of sequences in the input alignment as observed with the CASP datasets, was similar to what was observed for the artificial datasets (see abo […]


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NNcon institution(s)
Computer Science Department, Informatics Institute, University of Missouri, Columbia, MO, USA

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