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 VP  Vol.6 No.3 , September 2020
The Significant and Profound Impacts of Pseudo K-Tuple Nucleotide Composition
Abstract: In this short review paper, the significant and profound impacts of the “pseudo K-tuple nucleotide composition” have been briefly presented with crystal clear convincingness.

The “pseudo K-tuple nucleotide composition” or “PseKNC” [1], is an extended version of “pseudo amino acid composition” [2] or “PseAAC” [3].

Both PseAAC and PseKNC are of vector descriptor, but the former represents protein or peptide sequences while the latter represents DNA or RNA sequences.

Just like “PseAAC” (see, e.g., [4] - [35]) or “Pseudo amino acid composition” being very successful (see, e.g., [36] - [127]), it is indeed both significant and profound.

Cite this paper: Chou, K. (2020) The Significant and Profound Impacts of Pseudo K-Tuple Nucleotide Composition. Voice of the Publisher, 6, 91-101. doi: 10.4236/vp.2020.63009.
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[86]   Khosravian, M., Faramarzi, F.K., Beigi, M.M., Behbahani, M. and Mohabatkar, H. (2013) Predicting Antibacterial Peptides by the Concept of Chou’s Pseudo Amino Acid Composition and Machine Learning Methods. Protein & Peptide Letters, 20, 180-186.
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[87]   Lin, H., Ding, C., Yuan, L.-F., Chen, W., Ding, H., Li, Z.-Q., Guo, F.-B., Huang, J. and Rao, N. (2013) Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Optimal Tripeptide Composition. International Journal of Biomethmatics, 6, Article ID: 1350003.
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[88]   Liu, B., Wang, X., Zou, Q., Dong, Q. and Chen, Q. (2013) Protein Remote Homology Detection by Combining Chou’s Pseudo Amino Acid Composition and Profile-Based Protein Representation. Molecular Informatics, 32, 775-782.
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[89]   Mohabatkar, H., Beigi, M.M., Abdolahi, K. and Mohsenzadeh, S. (2013) Prediction of Allergenic Proteins by Means of the Concept of Chou’s Pseudo Amino Acid Composition and a Machine Learning Approach. Medicinal Chemistry, 9, 133-137.
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[90]   Qin, Y.F., Zheng, L. and Huang, J. (2013) Locating Apoptosis Proteins by Incorporating the Signal Peptide Cleavage Sites into the General Form of Chou’s Pseudo Amino Acid Composition. International Journal of Quantum Chemistry, 113, 1660-1667.
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[91]   Sarangi, A.N., Lohani, M. and Aggarwal, R. (2013) Prediction of Essential Proteins in Prokaryotes by Incorporating Various Physico-Chemical Features into the General Form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 20, 781-795.
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[92]   Wan, S., Mak, M.W. and Kung, S.Y. (2013) GOASVM: A Subcellular Location Predictor by Incorporating Term-Frequency Gene Ontology into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 323, 40-48.
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[93]   Wang, X., Li, G.Z. and Lu, W.C. (2013) Virus-ECC-mPLoc: A Multi-Label Predictor for Predicting the Subcellular Localization of Virus Proteins with Both Single and Multiple Sites Based on a General Form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 20, 309-317.
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[94]   Niu, X.H., et al. (2013) Using the Concept of Chou’s Pseudo Amino Acid Composition to Predict Protein Solubility: An Approach with Entropies in Information Theory. Journal of Theoretical Biology, 332, 211-217.
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[95]   Du, P., Gu, S. and Jiao, Y. (2014) PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo Amino Acid Composition for Large-Scale Protein Datasets. International Journal of Molecular Sciences, 15, 3495-3506.
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[96]   Hajisharifi, Z., Piryaiee, M., Mohammad Beigi, M., Behbahani, M. and Mohabatkar, H. (2014) Predicting Anticancer Peptides with Chou’s Pseudo Amino Acid Composition and Investigating Their Mutagenicity via Ames Test. Journal of Theoretical Biology, 341, 34-40.
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[97]   Jia, C., Lin, X. and Wang, Z. (2014) Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition. International Journal of Molecular Sciences, 15, 10410-10423.
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[98]   Kong, L., Zhang, L. and Lv, J. (2014) Accurate Prediction of Protein Structural Classes by Incorporating Predicted Secondary Structure Information into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 344, 12-18.
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[99]   Nanni, L., Brahnam, S. and Lumini, A. (2014) Prediction of Protein Structure Classes by Incorporating Different Protein Descriptors into General Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 360, 109-116.
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[100]   Zhang, J., Sun, P., Zhao, X. and Ma, Z. (2014) PECM: Prediction of Extracellular Matrix Proteins Using the Concept of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 363, 412-418.
https://doi.org/10.1016/j.jtbi.2014.08.002

[101]   Zhang, L., Zhao, X. and Kong, L. (2014) Predict Protein Structural Class for Low-Similarity Sequences by Evolutionary Difference Information into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 355, 105-110.
https://doi.org/10.1016/j.jtbi.2014.04.008

[102]   Zuo, Y.C., Peng, Y., Liu, L., Chen, W., Yang, L. and Fan, G.L. (2014) Predicting Peroxidase Subcellular Location by Hybridizing Different Descriptors of Chou’s Pseudo Amino Acid Patterns. Analytical Biochemistry, 458, 14-19.
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[103]   Ali, F. and Hayat, M. (2015) Classification of Membrane Protein Types Using Voting Feature Interval in Combination with Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 384, 78-83.
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[104]   Fan, G.L., Zhang, X.Y., Liu, Y.L., Nang, Y. and Wang, H. (2015) DSPMP: Discriminating Secretory Proteins of Malaria Parasite by Hybridizing Different Descriptors of Chou’s Pseudo Amino Acid Patterns. Journal of Computational Chemistry, 36, 2317-2327.
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[105]   Huang, C. and Yuan, J.Q. (2015) Simultaneously Identify Three Different Attributes of Proteins by Fusing Their Three Different Modes of Chou’s Pseudo Amino Acid Compositions. Protein & Peptide Letters, 22, 547-556.
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[106]   Khan, Z.U., Hayat, M. and Khan, M.A. (2015) Discrimination of Acidic and Alkaline Enzyme Using Chou’s Pseudo Amino Acid Composition in Conjunction with Probabilistic Neural Network Model. Journal of Theoretical Biology, 365, 197-203.
https://doi.org/10.1016/j.jtbi.2014.10.014

[107]   Kumar, R., Srivastava, A., Kumari, B. and Kumar, M. (2015) Prediction of Beta-Lactamase and Its Class by Chou’s Pseudo Amino Acid Composition and Support Vector Machine. Journal of Theoretical Biology, 365, 96-103.
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[108]   Wang, X., Zhang, W., Zhang, Q. and Li, G.Z. (2015) MultiP-SChlo: Multi-Label Protein Subchloroplast Localization Prediction with Chou’s Pseudo Amino Acid Composition and a Novel Multi-Label Classifier. Bioinformatics, 31, 2639-2645.
https://doi.org/10.1093/bioinformatics/btv212

[109]   Jiao, Y.S. and Du, P.F. (2016) Prediction of Golgi-Resident Protein Types Using General Form of Chou’s Pseudo Amino Acid Compositions: Approaches with Minimal Redundancy Maximal Relevance Feature Selection. Journal of Theoretical Biology, 402, 38-44.
https://doi.org/10.1016/j.jtbi.2016.04.032

[110]   Tang, H., Chen, W. and Lin, H. (2016) Identification of Immunoglobulins Using Chou’s Pseudo Amino Acid Composition with Feature Selection Technique. Molecular BioSystems, 12, 1269-1275.
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[111]   Zou, H.L. and Xiao, X. (2016) Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou’s Pseudo Amino Acid Compositions. The Journal of Membrane Biology, 249, 23-29.
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[112]   Huo, H., Li, T., Wang, S., Lv, Y., Zuo, Y. and Yang, L. (2017) Prediction of Presynaptic and Postsynaptic Neurotoxins by Combining Various Chou’s Pseudo Components. Scientific Reports, 7, Article No. 5827.
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[113]   Rahimi, M., Bakhtiarizadeh, M.R. and Mohammadi-Sangcheshmeh, A. (2017) OOgenesis_Pred: A Sequence-Based Method for Predicting Oogenesis Proteins by Six Different Modes of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 414, 128-136.
https://doi.org/10.1016/j.jtbi.2016.11.028

[114]   Tripathi, P. and Pandey, P.N. (2017) A Novel Alignment-Free Method to Classify Protein Folding Types by Combining Spectral Graph Clustering with Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 424, 49-54.
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[115]   Yu, B., Lou, L., Li, S., Zhang, Y., Qiu, W., Wu, X., Wang, M. and Tian, B. (2017) Prediction of Protein Structural Class for Low-Similarity Sequences Using Chou’s Pseudo Amino Acid Composition and Wavelet Denoising. Journal of Molecular Graphics and Modelling, 76, 260-273.
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[116]   Al Maruf, M.A. and Shatabda, S. (2018) iRSpot-SF: Prediction of Recombination Hotspots by Incorporating Sequence Based Features into Chou’s Pseudo Components. Genomics, 18, 63-82.

[117]   Arif, M., Hayat, M. and Jan, Z. (2018) iMem-2LSAAC: A Two-Level Model for Discrimination of Membrane Proteins and Their Types by Extending the Notion of SAAC into Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 442, 11-21.
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[118]   Cui, X., Yu, Z., Yu, B., Wang, M., Tian, B. and Ma, Q. (2018) UbiSitePred: A Novel Method for Improving the Accuracy of Ubiquitination Sites Prediction by Using LASSO to Select the Optimal Chou’s Pseudo Components. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 17, 512-538.

[119]   Mei, J. and Zhao, J. (2018) Prediction of HIV-1 and HIV-2 Proteins by Using Chou’s Pseudo Amino Acid Compositions and Different Classifiers. Scientific Reports, 8, Article No. 2359.
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[120]   Zhang, L. and Kong, L. (2018) iRSpot-ADPM: Identify Recombination Spots by Incorporating the Associated Dinucleotide Product Model into Chou’s Pseudo Components. Journal of Theoretical Biology, 441, 1-8.
https://doi.org/10.1016/j.jtbi.2017.12.025

[121]   Zhang, S., Yang, K., Lei, Y. and Song, K. (2018) iRSpot-DTS: Predict Recombination Spots by Incorporating the Dinucleotide-Based Spare-Cross Covariance Information into Chou’s Pseudo Components. Genomics, 11, 457-464.

[122]   Al Maruf, M.A. and Shatabda, S. (2019) iRSpot-SF: Prediction of Recombination Hotspots by Incorporating Sequence Based Features into Chou’s Pseudo Components. Genomics, 111, 966-972.
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[123]   Nosrati, M., Mohabatkar, H. and Behbahani, M. (2019) Introducing of an Integrated Artificial Neural Network and Chou’s Pseudo Amino Acid Composition Approach for Computational Epitope-Mapping of Crimean-Congo Haemorrhagic Fever Virus Antigens. International Immunopharmacology, 78, Article ID: 106020.
https://doi.org/10.1016/j.intimp.2019.106020

[124]   Pan, Y., Wang, S., Zhang, Q., Lu, Q., Su, D., Zuo, Y. and Yang, L. (2019) Analysis and Prediction of Animal Toxins by Various Chou’s Pseudo Components and Reduced Amino Acid Compositions. Journal of Theoretical Biology, 462, 221-229.
https://doi.org/10.1016/j.jtbi.2018.11.010

[125]   Tahir, M., Tayara, H. and Chong, K.T. (2019) iRNA-PseKNC(2methyl): Identify RNA 2’-O-Methylation Sites by Convolution Neural Network and Chou’s Pseudo Components. Journal of Theoretical Biology, 465, 1-6.
https://doi.org/10.1016/j.jtbi.2018.12.034

[126]   Tian, B., Wu, X., Chen, C., Qiu, W., Ma, Q. and Yu, B. (2019) Predicting Protein-Protein Interactions by Fusing Various Chou’s Pseudo Components and Using Wavelet Denoising Approach. Journal of Theoretical Biology, 462, 329-346.
https://doi.org/10.1016/j.jtbi.2018.11.011

[127]   Zhang, L. and Kong, L. (2019) iRSpot-PDI: Identification of Recombination Spots by Incorporating Dinucleotide Property Diversity Information into Chou’s Pseudo Components. Genomics, 111, 457-464.
https://doi.org/10.1016/j.ygeno.2018.03.003

 
 
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