Showcase to Illustrate How the Web-Server iKcr-PseEns is Working

Showcase to Illustrate How the Web-Server iKcr-PseEns is Working

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Author(s)

Author(s): Kuo-Chen Chou

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DOI: 10.18483/ijSci.2247 37 143 85-95 Volume 9 - Jan 2020

Abstract

In 2018 a very powerful web-server predictor has been established for predicting lysine crotonylation sites in histone proteins. See how the web-server is working.

Keywords

Web-Server, iKcr-PseEns

References

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  154. Y.F. Qin, C.H. Wang, X.Q. Yu, J. Zhu, T.G. Liu, X.Q. Zheng, Predicting Protein Structural Class by Incorporating Patterns of Over- Represented k-mers into the General form of Chou's PseAAC. Protein & Peptide Letters 19 (2012) 388-397.
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  171. M. Khosravian, F.K. Faramarzi, M.M. Beigi, M. Behbahani, H. Mohabatkar, Predicting Antibacterial Peptides by the Concept of Chou's Pseudo amino Acid Composition and Machine Learning Methods. Protein & Peptide Letters 20 (2013) 180-186.
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  174. B. Liu, X. Wang, Q. Zou, Q. Dong, Q. Chen, Protein remote homology detection by combining Chou's pseudo amino acid composition and profile-based protein representation. Molecular Informatics 32 (2013) 775-782.
  175. H. Mohabatkar, M.M. Beigi, K. Abdolahi, S. Mohsenzadeh, Prediction of Allergenic Proteins by Means of the Concept of Chou's Pseudo Amino Acid Composition and a Machine Learning Approach. Medicinal Chemistry 9 (2013) 133-137.
  176. E. Pacharawongsakda, T. Theeramunkong, Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC. IEEE Transactions on Nanobioscience 12 (2013) 311-320.
  177. Y.F. Qin, L. Zheng, J. Huang, 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 (2013) 1660-1667.
  178. A.N. Sarangi, M. Lohani, R. Aggarwal, Prediction of Essential Proteins in Prokaryotes by Incorporating Various Physico-chemical Features into the General form of Chou's Pseudo Amino Acid Composition. Protein Pept Lett 20 (2013) 781-95.
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  180. X. Wang, G.Z. Li, W.C. Lu, 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 (2013) 309-317.
  181. X. Xiao, J.L. Min, P. Wang, K.C. Chou, iCDI-PseFpt: Identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. Journal of Theoretical Biology 337C (2013) 71-79.
  182. N. Xiaohui, L. Nana, X. Jingbo, C. Dingyan, P. Yuehua, X. Yang, W. Weiquan, W. Dongming, W. Zengzhen, 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 (2013) 211-217.
  183. H.L. Xie, L. Fu, X.D. Nie, Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC. Protein Eng Des Sel 26 (2013) 735-742.
  184. Y. Xu, J. Ding, L.Y. Wu, K.C. Chou, iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition PLoS ONE 8 (2013) e55844.
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  186. P. Du, S. Gu, Y. Jiao, 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 (2014) 3495-3506.
  187. Z. Hajisharifi, M. Piryaiee, M. Mohammad Beigi, M. Behbahani, H. Mohabatkar, Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test. Journal of Theoretical Biology 341 (2014) 34-40.
  188. G.S. Han, Z.G. Yu, V. Anh, A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC. J Theor Biol 344 (2014) 31-9.
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  191. L. Kong, L. Zhang, J. Lv, Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition. J Theor Biol 344 (2014) 12-18.
  192. L. Li, S. Yu, W. Xiao, Y. Li, M. Li, L. Huang, X. Zheng, S. Zhou, H. Yang, Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach. Biochimie 104 (2014) 100-7.
  193. B. Liu, J. Xu, X. Lan, R. Xu, J. Zhou, X. Wang, K.C. Chou, iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS ONE 9 (2014) e106691.
  194. S. Mondal, P.P. Pai, Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol 356 (2014) 30-5.
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  196. W.R. Qiu, X. Xiao, K.C. Chou, iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci (IJMS) 15 (2014) 1746-1766.
  197. W.R. Qiu, X. Xiao, W.Z. Lin, K.C. Chou, iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach. Biomed Res Int (BMRI) 2014 (2014) 947416.
  198. Y. Xu, X. Wen, X.J. Shao, N.Y. Deng, K.C. Chou, iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. International Journal of Molecular Sciences (IJMS) 15 (2014) 7594-7610.
  199. Y. Xu, X. Wen, L.S. Wen, L.Y. Wu, N.Y. Deng, K.C. Chou, iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE 9 (2014) e105018.
  200. J. Zhang, P. Sun, X. Zhao, Z. Ma, PECM: Prediction of extracellular matrix proteins using the concept of Chou's pseudo amino acid composition. Journal of Theoretical Biology 363 (2014) 412-418.
  201. J. Zhang, X. Zhao, P. Sun, Z. Ma, PSNO: Predicting Cysteine S-Nitrosylation Sites by Incorporating Various Sequence-Derived Features into the General Form of Chou's PseAAC. Int J Mol Sci 15 (2014) 11204-19.
  202. L. Zhang, X. Zhao, L. Kong, Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou's pseudo amino acid composition. J Theor Biol 355 (2014) 105-10.
  203. Y.C. Zuo, Y. Peng, L. Liu, W. Chen, L. Yang, G.L. Fan, Predicting peroxidase subcellular location by hybridizing different descriptors of Chou's pseudo amino acid patterns. Anal Biochem 458 (2014) 14-9.
  204. S. Ahmad, M. Kabir, M. Hayat, Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC. Comput Methods Programs Biomed 122 (2015) 165-74.
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  207. Dehzangi, R. Heffernan, A. Sharma, J. Lyons, K. Paliwal, A. Sattar, Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC. J Theor Biol 364 (2015) 284-294.
  208. G.L. Fan, X.Y. Zhang, Y.L. Liu, Y. Nang, H. Wang, DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. J Comput Chem 36 (2015) 2317-27.
  209. C. Huang, J.Q. Yuan, Simultaneously Identify Three Different Attributes of Proteins by Fusing their Three Different Modes of Chou's Pseudo Amino Acid Compositions. Protein Pept Lett 22 (2015) 547-56.
  210. J. Jia, Z. Liu, X. Xiao, K.C. Chou, iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol 377 (2015) 47-56.
  211. Z. Ju, J.Z. Cao, H. Gu, iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chous general PseAAC. J Theor Biol 385 (2015) 50-7.
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  213. R. Kumar, A. Srivastava, B. Kumari, M. Kumar, Prediction of beta-lactamase and its class by Chou's pseudo amino acid composition and support vector machine. J Theor Biol 365 (2015) 96-103.
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  215. B. Liu, J. Xu, S. Fan, R. Xu, J. Jiyun Zhou, X. Wang, PseDNA-Pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance transformation. Molecular Informatics 34 (2015) 8-17
  216. M. Mandal, A. Mukhopadhyay, U. Maulik, Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC. Med Biol Eng Comput 53 (2015) 331-44.
  217. V. Sanchez, A.M. Peinado, J.L. Perez-Cordoba, A.M. Gomez, A new signal characterization and signal-based Chou's PseAAC representation of protein sequences. J Bioinform Comput Biol 13 (2015) 1550024.
  218. R. Sharma, A. Dehzangi, J. Lyons, K. Paliwal, T. Tsunoda, A. Sharma, Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou's General PseAAC. IEEE Trans Nanobioscience 14 (2015) 915-26.
  219. X. Wang, W. Zhang, Q. Zhang, G.Z. Li, MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier. Bioinformatics 31 (2015) 2639-45.
  220. R. Xu, J. Zhou, B. Liu, Y.A. He, Q. Zou, X. Wang, K.C. Chou, Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. Journal of Biomolecular Structure & Dynamics (JBSD) 33 (2015) 1720-1730.
  221. M. Zhang, B. Zhao, X. Liu, Predicting industrial polymer melt index via incorporating chaotic characters into Chou's general PseAAC. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB) 146 (2015) 232-240.
  222. S.L. Zhang, Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB) 142 (2015) 28-35.
  223. P.P. Zhu, W.C. Li, Z.J. Zhong, E.Z. Deng, H. Ding, W. Chen, H. Lin, Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol Biosyst 11 (2015) 558-63.
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  225. M. Behbahani, H. Mohabatkar, M. Nosrati, Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou's general pseudo amino acid composition. J Theor Biol 411 (2016) 1-5.
  226. G.L. Fan, Y.L. Liu, H. Wang, Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition. J Theor Biol 407 (2016) 138-142.
  227. J. Jia, Z. Liu, X. Xiao, B. Liu, K.C. Chou, Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC). J Biomol Struct Dyn (JBSD) 34 (2016) 1946-1961.
  228. J. Jia, Z. Liu, X. Xiao, B. Liu, K.C. Chou, pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. Journal of Theoretical Biology 394 (2016) 223-230.
  229. J. Jia, Z. Liu, X. Xiao, B. Liu, K.C. Chou, iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 7 (2016) 34558-34570.
  230. J. Jia, L. Zhang, Z. Liu, X. Xiao, K.C. Chou, pSumo-CD: Predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics 32 (2016) 3133-3141.
  231. Y.S. Jiao, P.F. Du, Prediction of Golgi-resident protein types using general form of Chou's pseudo amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection. J Theor Biol 402 (2016) 38-44.
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  234. W.R. Qiu, B.Q. Sun, X. Xiao, Z.C. Xu, K.C. Chou, iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 7 (2016) 44310-44321.
  235. M. Tahir, M. Hayat, iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC. Mol Biosyst 12 (2016) 2587-93.
  236. H. Tang, W. Chen, H. Lin, Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique. Mol Biosyst 12 (2016) 1269-1275.
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  239. H.L. Zou, X. Xiao, Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions. J Membr Biol 249 (2016) 23-9.
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  241. X. Cheng, X. Xiao, K.C. Chou, pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Molecular BioSystems 13 (2017) 1722-1727.
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  244. Z. Ju, J.J. He, Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC. J Mol Graph Model 77 (2017) 200-204.
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  247. L.M. Liu, Y. Xu, K.C. Chou, iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general PseAAC. Med Chem 13 (2017) 552-559.
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  249. W.R. Qiu, B.Q. Sun, X. Xiao, D. Xu, K.C. Chou, iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Molecular Informatics 36 (2017) UNSP 1600010.
  250. W.R. Qiu, Q.S. Zheng, B.Q. Sun, X. Xiao, Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory. Mol Inform 36 (2017) UNSP 1600085.
  251. M. Rahimi, M.R. Bakhtiarizadeh, A. Mohammadi-Sangcheshmeh, OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou's pseudo amino acid composition. J Theor Biol 414 (2017) 128-136.
  252. P. Tripathi, P.N. Pandey, A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition. J Theor Biol 424 (2017) 49-54.
  253. X. Xiao, X. Cheng, S. Su, Q. Nao, K.C. Chou, pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Natural Science 9 (2017) 331-349.
  254. C. Xu, L. Ge, Y. Zhang, M. Dehmer, I. Gutman, Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC. J Biomed Inform doi:10.1016/j.jbi.2017.09.011 (2017).
  255. Y. Xu, C. Li, K.C. Chou, iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med Chem 13 (2017) 544-551.
  256. B. Yu, S. Li, W.Y. Qiu, C. Chen, R.X. Chen, L. Wang, M.H. Wang, Y. Zhang, Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising. Oncotarget 8 (2017) 107640-107665.
  257. B. Yu, L. Lou, S. Li, Y. Zhang, W. Qiu, X. Wu, M. Wang, B. Tian, Prediction of protein structural class for low-similarity sequences using Chou's pseudo amino acid composition and wavelet denoising. J Mol Graph Model 76 (2017) 260-273.
  258. J. Ahmad, M. Hayat, MFSC: Multi-voting based Feature Selection for Classification of Golgi Proteins by Adopting the General form of Chou's PseAAC components. J Theor Biol 463 (2018) 99-109.
  259. S. Akbar, M. Hayat, iMethyl-STTNC: Identification of N(6)-methyladenosine sites by extending the Idea of SAAC into Chou's PseAAC to formulate RNA sequences. J Theor Biol 455 (2018) 205-211.
  260. M. Arif, M. Hayat, Z. Jan, 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. J Theor Biol 442 (2018) 11-21.
  261. A.H. Butt, N. Rasool, Y.D. Khan, Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC. Mol Biol Rep doi:10.1007/s11033-018-4391-5 (2018).
  262. X. Cheng, X. Xiao, K.C. Chou, pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110 (2018) 50-58.
  263. X. Cheng, X. Xiao, K.C. Chou, pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 110 (2018) 231-239.
  264. X. Cheng, X. Xiao, K.C. Chou, pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34 (2018) 1448-1456.
  265. X. Cheng, X. Xiao, K.C. Chou, pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. Journal of Theoretical Biology 458 (2018) 92-102.
  266. X. Cheng, X. Xiao, K.C. Chou, pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset Curr Pharm Des 24 (2018) 4013-4022.
  267. E. Contreras-Torres, Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC. J Theor Biol 454 (2018) 139-145.
  268. X. Fu, W. Zhu, B. Liso, L. Cai, L. Peng, J. Yang, Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC. IEEE Access 20 (2018) https://doi.org/10.1109/ACCESS.2018.2876656.
  269. A.W. Ghauri, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou's general PseAAC. Curr Pharm Des 24 (2018) 4034-4043.
  270. F. Javed, M. Hayat, Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics https://doi.org/10.1016/j.ygeno.2018.09.004 (2018).
  271. Z. Ju, S.Y. Wang, Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition. Gene 664 (2018) 78-83.
  272. Y.D. Khan, N. Rasool, W. Hussain, S.A. Khan, K.C. Chou, iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Analytical Biochemistry 550 (2018) 109-116.
  273. Y.D. Khan, N. Rasool, W. Hussain, S.A. Khan, K.C. Chou, iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol Biol Rep 45 (2018) 2501-2509.
  274. M.S. Krishnan, Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 445 (2018) 62-74.
  275. Y. Liang, S. Zhang, Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence. J Theor Biol 454 (2018) 22-29.
  276. J. Mei, Y. Fu, J. Zhao, Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition. J Theor Biol 456 (2018) 41-48.
  277. J. Mei, J. Zhao, Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers. Sci Rep 8 (2018) 2359.
  278. J. Mei, J. Zhao, Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features. J Theor Biol 427 (2018) 147-153.
  279. M. Mousavizadegan, H. Mohabatkar, Computational prediction of antifungal peptides via Chou's PseAAC and SVM. J Bioinform Comput Biol (2018) 1850016.
  280. S.M. Rahman, S. Shatabda, S. Saha, M. Kaykobad, M. Sohel Rahman, DPP-PseAAC: A DNA-binding Protein Prediction model using Chou's general PseAAC. J Theor Biol 452 (2018) 22-34.
  281. E.S. Sankari, D.D. Manimegalai, Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC. J Theor Biol 455 (2018) 319-328.
  282. Srivastava, R. Kumar, M. Kumar, BlaPred: predicting and classifying beta-lactamase using a 3-tier prediction system via Chou's general PseAAC. J Theor Biol 457 (2018) 29-36.
  283. S. Zhang, X. Duan, Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC. J Theor Biol 437 (2018) 239-250.
  284. S. Zhang, Y. Liang, Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC. J Theor Biol 457 (2018) 163-169.
  285. S. Adilina, D.M. Farid, S. Shatabda, Effective DNA binding protein prediction by using key features via Chou's general PseAAC. J Theor Biol 460 (2019) 64-78.
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  287. M. Awais, W. Hussain, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition. IEEE/ACM Trans Comput Biol Bioinform https://doi.org/10.1109/TCBB.2019.2919025 or https://www.ncbi.nlm.nih.gov/pubmed/31144645 (2019).
  288. M. Behbahani, M. Nosrati, M. Moradi, H. Mohabatkar, Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule. Applied Biochemistry and Biotechnology doi:10.1007/s12010-019-03141-8 (2019).
  289. A.H. Butt, N. Rasool, Y.D. Khan, Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC. Journal of Theoretical Biology 473 (2019) 1-8.
  290. G. Chen, M. Cao, J. Yu, X. Guo, S. Shi, Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC. J Theor Biol 461 (2019) 92-101.
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  293. F. Javed, M. Hayat, Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics 111 (2019) 1325-1332.
  294. S.J. Malebary, M.S.U. Rehman, Y.D. Khan, iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule. PLoS One 14 (2019) e0223993.
  295. Q. Ning, Z. Ma, X. Zhao, dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components. J Theor Biol 470 (2019) 43-49.
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  298. M. Tahir, M. Hayat, S.A. Khan, iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition. Mol Genet Genomics 294 (2019) 199-210.
  299. L. Wang, R. Zhang, Y. Mu, Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC. J Theor Biol 461 (2019) 51-58.
  300. X. Xiao, X. Cheng, G. Chen, Q. Mao, K.C. Chou, pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 15 (2019) 496-509.
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