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iDNAPro-PseAAC specifications


Unique identifier OMICS_11858
Name iDNAPro-PseAAC
Interface Web user interface
Restrictions to use None
Input data Protein sequences
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Bin Liu

Publication for iDNAPro-PseAAC

iDNAPro-PseAAC citations


On the prediction of DNA binding proteins only from primary sequences: A deep learning approach

PLoS One
PMCID: 5747425
PMID: 29287069
DOI: 10.1371/journal.pone.0188129

[…] ncorporating amino acid distance-pairs and reducing alphabet profiles into the general pseudo amino acid composition [], PseDNA-Pro by combining PseAAC and physiochemical distance transformations [], iDNAPro-PseAAC by combining pseudo amino acid composition and profile-based protein representation [], iDNA-KACC by combining auto-cross covariance transformation and ensemble learning []. Zhou et al. […]


HMMBinder: DNA Binding Protein Prediction Using HMM Profile Based Features

Biomed Res Int
PMCID: 5706079
PMID: 29270430
DOI: 10.1155/2017/4590609

[…] with several previous methods and tools used for DNA-binding protein prediction on the benchmark dataset benchmark1075. They are DNABinder [], DNA-Prot [], iDNA-Prot [], iDNA-Prot|dis [], DBPPred [], iDNAPro-PseAAC [], PseDNA-Pro [], Kmer1 + ACC [], and Local-DPP []. The results reported in this paper for these methods are taken from [, ]. The comparisons were made in terms of accuracy, sensitivit […]


iDNAProt ES: Identification of DNA binding Proteins Using Evolutionary and Structural Features

Sci Rep
PMCID: 5668250
PMID: 29097781
DOI: 10.1038/s41598-017-14945-1

[…] . A number of softwares, web-servers and prediction methods are available in the literature for DNA-binding protein prediction. Among them are: DNABinder, DNA-Prot, iDNA-Prot, iDNA-Prot|dis, DBPPred, iDNAPro-PseAAC, PseDNA-Pro, Kmer1 + ACC, Local-DPP, etc. Kumar et al. used evolutionary information from PSSM profiles with support vector machines and established a web-server called DNABinder. They […]


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

[…] ey incorporated features of overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. Liu et al. proposed a predictor called iDNAPro-PseAAC [] which used PseAAC feature Combined with SVM algorithm. The most recent prediction method for DNA-binding proteins was aslo proposed Liu et al.which called iDNA-KACC[]. The iDNA-KACC […]

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iDNAPro-PseAAC institution(s)
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
iDNAPro-PseAAC funding source(s)
This work was supported by the National Natural Science Foundation of China (No. 61300112 and 61272383), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Natural Science Foundation of Guangdong Province (2014A030313695), Strategic Emerging Industry Development Special Funds of Shenzhen (JCYJ20140508161040764), and National High Technology Research and Development Program of China (863 Program) [2015AA015405].

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