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Citations per year

Number of citations per year for the bioinformatics software tool nDNA-Prot

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This map represents all the scientific publications referring to nDNA-Prot per scientific context
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nDNA-Prot specifications


Unique identifier OMICS_11859
Name nDNA-Prot
Interface Web user interface
Restrictions to use None
Input data Protein sequences
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Guo L.

Publication for nDNA-Prot

nDNA-Prot citations


Predicting Variation of DNA Shape Preferences in Protein DNA Interaction in Cancer Cells with a New Biophysical Model

PMCID: 5615366
PMID: 28927002
DOI: 10.3390/genes8090233

[…] the development of many approximate models to compute TF–DNA binding affinity. There are models that aim at identifying proteins that may bind to DNA based on the protein amino acid sequences (e.g., nDNA-Prot []) or models that focus on the prediction of protein target sites (e.g., BayesPI2 []). The latter ones are useful in identifying functional TF binding sites, predicting the effects of mutat […]


Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

PLoS One
PMCID: 5524285
PMID: 28738059
DOI: 10.1371/journal.pone.0179805

[…] on spots by fusing different modes of pseudo K-tuple nucleotide composition and mode of dinucleotide-based auto-cross covariance. Song et al. [] employed an ensemble classifier using a new predictor (nDNA-Prot) to obtain the protein structure and identify DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). Wang e […]


An information based network approach for protein classification

PLoS One
PMCID: 5370107
PMID: 28350835
DOI: 10.1371/journal.pone.0174386

[…] for support vector machines that has gained wide popularity in machine learning and many other areas []. LibD3C is an ensemble classifier which is based on clustering and parallel implementation []. nDNA-Prot proposed in [], is a new predictor to accurately identify DNA-binding proteins when combines with an ensemble classifier. Also, an improved protein structural classes predictor is proposed i […]


DNABP: Identification of DNA Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

PLoS One
PMCID: 5132331
PMID: 27907159
DOI: 10.1371/journal.pone.0167345

[…] ind of amino acid and physicochemical properties as input features. We used the Testset to evaluate our DNABP in comparison with the other three methods mentioned above. enDNA-Port, iDNA-Prot|dis and nDNA-Prot could predict DNA-binding proteins on the web server; therefore, the Testset was submitted to those three web servers for prediction. As shown in , the enDNA-Port achieved an MCC of 0.183 wi […]

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nDNA-Prot institution(s)
School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China
nDNA-Prot funding source(s)
This work was supported by the Natural Science Foundation of China (No. 61370010, No.61202011, No.81101115, No.61301251).

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