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iDNA-Prot specifications

Information


Unique identifier OMICS_18563
Name iDNA-Prot
Interface Web user interface
Restrictions to use None
Input data Some sequences of proteins (maximum 50 proteins).
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes

Documentation


Maintainer


  • person_outline Xuan Xiao

Publication for iDNA-Prot

iDNA-Prot citations

 (12)
library_books

Protein Sequence Comparison and DNA binding Protein Identification with Generalized PseAAC and Graphical Representation

2018
PMCID: 5930480
PMID: 29380690
DOI: 10.2174/1386207321666180130100838

[…] y (see Table ). This demonstrates that our SVM model performs equally well on independent dataset. For convenience of comparison, results of some existing methods including DNAbinder [], DNA-Prot [], iDNA-Prot [] and enDNA-Prot [] are also listed in Table . DNAbinder developed by Kumar et al. [] can extract evolutionary information in form of position specific scoring matrix (PSSM) from the corres […]

library_books

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

2017
PLoS One
PMCID: 5747425
PMID: 29287069
DOI: 10.1371/journal.pone.0188129

[…] bution (D)for extracting seven physiochemical characters of amino acids []. Kumar et al. trained a SVM model using amino acid composition and evolutionary information in the form of PSSM profiles []. iDNA-Prot used random forest algorithm as the predictor engine by incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via a “gr […]

library_books

HMMBinder: DNA Binding Protein Prediction Using HMM Profile Based Features

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

[…] have compared the performance of HMMBinder 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 comparison […]

library_books

Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO SVM algorithm

2017
PLoS One
PMCID: 5667738
PMID: 29095904
DOI: 10.1371/journal.pone.0187371

[…] nel parameters, the identification of DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition upon a new predictor (iDNA-Prot|dis) outperformed the existing predictors for the same purpose []. Liu et al. also reported that each kernel contains different discriminative information and that combining the kernels auto […]

library_books

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

2017
Sci Rep
PMCID: 5668250
PMID: 29097781
DOI: 10.1038/s41598-017-14945-1

[…] ed amino acid composition, physio-chemical properties and secondary structure information as features and trained their model using a Random Forest classifier. Lin et al. presented a web-server named iDNA-Prot where they used grey model to incorporate amino acid sequence as features into the general form of pseudo amino acid composition and trained their model using Random Forest classifier. Amino […]

library_books

Investigation of the inhibition effect and mechanism of myricetin to Suilysin by molecular modeling

2017
Sci Rep
PMCID: 5603505
PMID: 28924148
DOI: 10.1038/s41598-017-12168-y

[…] ance matrix C of the atomic coordinates. Some other similar approaches also can be used to predict the motion of protein, such as the so-called profile-based protein representation, iPro54-PseKNC and iDNA-Prot–.According to previous reports, the conformational change of SLY should be complete to achieve haemolytic activity through monomeric oligomerization–. In the present study, the haemolytic ac […]

Citations

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iDNA-Prot institution(s)
Information Science and Technology School, Donghua University, Shanghai, China; Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; Gordon Life Science Institute, San Diego, California, USA
iDNA-Prot funding source(s)
Supported by the grants from the National Natural Science Foundation of China (60961003), the Key Project of Chinese Ministry of Education (210116), the Province National Natural Science Foundation of JiangXi (2009GZS0064 and 2010GZS0122), and the department of education of Jiang-Xi Province (GJJ09271).

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