ural-network and B
ayes-classifier based co
ntact prediction) is a hierarchical algorithm for sequence-based protein contact map prediction.
It first uses the naive Bayes classifier theorem to calculate the posterior probability of
eight machine-learning and co-evoluation based
contact prodiction programs (SVMSEQ, BETACON, SVMcon, PSICOV, CCMpred,
FreeContact, MetaPSICOV, and STRUCTCH).
Final contact maps are then created by neural network machine that
trains the posterior probability scores with intrinsic structural features
from secondary structure, solvent accessibility, and Shannon entropy of multiple sequence
NeBcon On-line (
view an example of NeBcon output)
The standalone NeBcon package can be downloaded from
NeBconpackage.tar.gz. In order to install and run the package, follow the instrunctions below:
1. Decompress the NeBconpackage.tar.gz with the command: tar -zxvf NeBconpackage.tar.gz
2. After decompressing, read the
README.txt file, which is available in NeBconpackage, for further instruction to run the program.
Baoji He, S M Mortuza, Yanting Wang, Hongbin Shen, Yang Zhang.
NeBcon: Protein contact map prediction using neural network training coupled with naïve Bayes classifiers. Bioinformatics,
DOI: https://doi.org/10.1093/bioinformatics/btx164, 2017.
[PDF] [Support Information]
A list of the training dataset (517 non-homolgous proteins) and the test dataset (98 proteins)
can be found at