Recently, the life of worldwide human beings has been endangering by the spreading of pneu - monia - causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep - learning” technique and develop ed a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web- server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/ , by which the majority of experimental scientists can easily get their desired data.
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Wiktorowicz, A., Wit, A., Dziewierz, A., Rzeszutko, L., Dudek, D. and Kleczynski, P. (2019) Calcium Pattern Assessment in Patients with Severe Aortic Stenosis via the Chou’s 5-Steps Rule. Current Pharmaceutical Design, 25, 3769-3775. https://doi.org/10.2174/1381612825666190930101258
Yang, L., Lv, Y., Wang, S.Y., Q., Zhang, Y., Pan, D., Su, Q. and Lu, Zuo, Y. (2019) Identifying FL11 Subtype by Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule. Genomics, 112, 1500-1515. https://doi.org/10.1016/j.ygeno.2019.08.021
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Mohammad, B.M., Behjati, M. and Mohabatkar, H. (2011) Prediction of Metalloproteinase Family Based on the Concept of Chou’s Pseudo Amino Acid Composition Using a Machine Learning Approach. Journal of Structural and Functional Genomics, 12, 191-197. https://doi.org/10.1007/s10969-011-9120-4
Qiu, J.D., Suo, S.B., Sun, X.Y., Shi, S.P. and Liang, R.P. (2011) OligoPred: A Web-Server for Predicting Homo-Oligomeric Proteins by Incorporating Discrete Wavelet Transform into Chou’s Pseudo Amino Acid Composition. Journal of Molecular Graphics & Modelling, 30, 129-134. https://doi.org/10.1016/j.jmgm.2011.06.014
Zou, D., He, Z., He, J. and Xia, Y. (2011) Supersecondary Structure Prediction Using Chou’s Pseudo Amino Acid Composition. Journal of Computational Chemistry, 32, 271-278. https://doi.org/10.1002/jcc.21616
Cao, J.Z. Liu, W.Q. and Gu, H. (2012) Predicting Viral Protein Subcellular Localization with Chou’s Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm. Protein and Peptide Letters, 19, 1163-1169. https://doi.org/10.2174/092986612803216999
Chen, C., Shen, Z.B. and Zou, X.Y. (2012) Dual-Layer Wavelet SVM for Predicting Protein Structural Class via the General Form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 19, 422-429. https://doi.org/10.2174/092986612799789332
Du, P., Wang, X., Xu, C. and Gao, Y. (2012) PseAAC-Builder: A Cross-Platform Stand-Alone Program for Generating Various Special Chou’s Pseudo Amino Acid Compositions. Analytical Biochemistry, 425, 117-119. https://doi.org/10.1016/j.ab.2012.03.015
Fan, G.L. and Li, Q.Z. (2012) Predict Mycobacterial Proteins Subcellular Locations by Incorporating Pseudo-Average Chemical Shift into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 304, 88-95. https://doi.org/10.1016/j.jtbi.2012.03.017
Fan, G.L. and Li, Q.Z. (2012) Predicting Protein Submitochondria Locations by Combining Different Descriptors into the General Form of Chou’s Pseudo Amino Acid Composition. Amino Acids, 43, 545-555. https://doi.org/10.1007/s00726-011-1143-4
Li, L.Q., Zhang, Y., Zou, L.Y., Zhou, Y. and Zheng, X.Q. (2012) Prediction of Protein Subcellular Multi-Localization Based on the General form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 19, 375-387. https://doi.org/10.2174/092986612799789369
Liu, L., Hu, X.Z., Liu, X.X., Wang, Y. and Li, S.B. (2012) Predicting Protein Fold Types by the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Optimal Feature Extractions. Protein & Peptide Letters, 19, 439-449. https://doi.org/10.2174/092986612799789378
Nanni, L., Brahnam, S. and Lumini, A. (2012) Wavelet Images and Chou’s Pseudo Amino Acid Composition for Protein Classification. Amino Acids, 43, 657-665. https://doi.org/10.1007/s00726-011-1114-9
Nanni, L., Lumini, A., Gupta, D. and Garg, A. (2012) Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou’s Pseudo Amino Acid Composition and on Evolutionary Information. IEEE/ACM Transaction on Computational Biology and Bioinformatics, 9, 467-475. https://doi.org/10.1109/TCBB.2011.117
Niu, X.H., Hu, X.H., Shi, F. and Xia, J.B. (2012) Predicting Protein Solubility by the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Fractal Dimension. Protein & Peptide Letters, 19, 940-948. https://doi.org/10.2174/092986612802084492
Ren, L.Y., Zhang, Y.S. and Gutman, I. (2012) Predicting the Classification of Transcription Factors by Incorporating Their Binding Site Properties into a Novel Mode of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 19, 1170-1176. https://doi.org/10.2174/092986612803217088
Zhao, X.W., Ma, Z.Q. and Yin, M.H. (2012) Predicting Protein-Protein Interactions by Combing Various Sequence-Derived Features into the General form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 19, 492-500. https://doi.org/10.2174/092986612800191080
Zia-ur-Rehman and Khan, A. (2012) Identifying GPCRs and Their Types with Chou’s Pseudo Amino Acid Composition: An Approach from Multi-Scale Energy Representation and Position Specific Scoring Matrix. Protein & Peptide Letters, 19, 890-903. https://doi.org/10.2174/092986612801619589
Chen, Y.K. and Li, K.B. (2013) Predicting Membrane Protein Types by Incorporating Protein Topology, Domains, Signal Peptides, and Physicochemical Properties into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 318, 1-12. https://doi.org/10.1016/j.jtbi.2012.10.033
Fan, G.L. and Li, Q.Z. (2013) Discriminating Bioluminescent Proteins by Incorporating Average Chemical Shift and Evolutionary Information into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 334, 45-51. https://doi.org/10.1016/j.jtbi.2013.06.003
Georgiou, D.N., Karakasidis, T.E. and Megaritis, A.C. (2013) A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and Its Combination with Fuzzy Set Theory. The Open Bioinformatics Journal, 7, 41-48. https://doi.org/10.2174/1875036201307010041
Gupta, M.K., Niyogi, R. and Misra, M. (2013) An Alignment-Free Method to Find Similarity among Protein Sequences via the General Form of Chou’s Pseudo Amino Acid Composition. SAR and QSAR in Environmental Research, 24, 597-609. https://doi.org/10.1080/1062936X.2013.773378
Huang, C. and Yuan, J. (2013) Using Radial Basis Function on the General Form of Chou’s Pseudo Amino Acid Composition and PSSM to Predict Subcellular Locations of Proteins with Both Single and Multiple Sites. Biosystems, 113, 50-57. https://doi.org/10.1016/j.biosystems.2013.04.005
Huang, C. and Yuan, J.Q. (2013) A Multilabel Model Based on Chou’s Pseudo Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types. The Journal of Membrane Biology, 246, 327-334. https://doi.org/10.1007/s00232-013-9536-9
Huang, C. and Yuan, J.Q. (2013) Predicting Protein Subchloroplast Locations with Both Single and Multiple Sites via Three Different Modes of Chou’s Pseudo Amino Acid Compositions. Journal of Theoretical Biology, 335, 205-212. https://doi.org/10.1016/j.jtbi.2013.06.034
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. https://doi.org/10.2174/092986613804725307
Lin, H., Ding, C., Yuan, L.F., Chen, W., Ding, H., Li, Z. Q., Guo, F.-B., Huang, J. and Rao, N.-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 Biomathematics, 6, Article ID: 1350003. https://doi.org/10.1142/S1793524513500034
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. https://doi.org/10.1002/minf.201300084
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. https://doi.org/10.2174/157340613804488341
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. https://doi.org/10.1002/qua.24383
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 and Peptide Letters, 20, 781-795. https://doi.org/10.2174/0929866511320070008
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. https://doi.org/10.1016/j.jtbi.2013.01.012
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. https://doi.org/10.2174/092986613804910608
Niu, X.H., Li, N.N., Xia, J.B., Chen, D.Y., Peng, Y.H., Xiao, Y., Wei, W.Q., Wang, D.M. and Wang, Z.Z. (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. https://doi.org/10.1016/j.jtbi.2013.03.010
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. https://doi.org/10.3390/ijms15033495
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. https://doi.org/10.1016/j.jtbi.2013.08.037
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. https://doi.org/10.3390/ijms150610410
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. https://doi.org/10.1016/j.jtbi.2013.11.021
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. https://doi.org/10.1016/j.jtbi.2014.07.003
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
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
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. https://doi.org/10.1016/j.ab.2014.04.032
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. https://doi.org/10.1016/j.jtbi.2015.07.034
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. https://doi.org/10.1002/jcc.24210
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 and Peptide Letters, 22, 547-556. https://doi.org/10.2174/0929866522666150209151344
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, 36, 197-203. https://doi.org/10.1016/j.jtbi.2014.10.014
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. https://doi.org/10.1016/j.jtbi.2014.10.008
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
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
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. https://doi.org/10.1039/C5MB00883B
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. https://doi.org/10.1007/s00232-015-9830-9
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. https://doi.org/10.1038/s41598-017-06195-y
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
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. https://doi.org/10.1016/j.jtbi.2017.04.027
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. https://doi.org/10.1016/j.jmgm.2017.07.012
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. https://doi.org/10.1016/j.ygeno.2018.06.003
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. https://doi.org/10.1016/j.jtbi.2018.01.008
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, 184, 28-43. https://doi.org/10.1016/j.chemolab.2018.11.012
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. https://doi.org/10.1038/s41598-018-20819-x
Qiu, W., Li, S., Cui, X., Yu, Z., Wang, M., Du, J., Peng, Y. and Yu, B. (2018) Predicting Protein Submitochondrial Locations by Incorporating the Pseudo-Position Specific Scoring Matrix into the General Chou’s Pseudo-Amino Acid Composition. Journal of Theoretical Biology, 450, 86-103. https://doi.org/10.1016/j.jtbi.2018.04.026
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
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.
Zhao, W., Wang, L., Zhang, T.X., Zhao, Z.N. and Du, P.F. (2018) A Brief Review on Software Tools in Generating Chou’s Pseudo-Factor Representations for All Types of Biological Sequences. Protein and Peptide Letters, 25, 822-829. https://doi.org/10.2174/0929866525666180905111124
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. https://doi.org/10.1016/j.ygeno.2018.06.003
Nosrati, M. Mohabatkar, H. and Behbahani, M. (2020) 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://www.sciencedirect.com/science/article/pii/S1567576919321277 https://doi.org/10.1016/j.intimp.2019.106020
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
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
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
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
Zhang, S., Yang, K., Lei, Y. and Song, K. (2019) iRSpot-DTS: Predict Recombination Spots by Incorporating the Dinucleotide-Based Spare-Cross Covariance Information into Chou’s Pseudo Components. Genomics, 111, 1760- 1770. https://doi.org/10.1016/j.ygeno.2018.11.031
Nosrati, M., Mohabatkar, H. and Behbahani, M. (2020) 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
Tahir, M., Hayat, M. and Khan, S.A. (2019) 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. Molecular Genetics and Genomics, 294, 199-210. https://doi.org/10.1007/s00438-018-1498-2
Javed, F. and Hayat, M. (2019) Predicting Subcellular Localization of Multi-Label Proteins by Incorporating the Sequence Features into Chou’s PseAAC. Genomics, 111, 1325-1332. https://doi.org/10.1016/j.ygeno.2018.09.004
Butt, A.H., Rasool, N. and Khan, Y.D. (2019) Prediction of Antioxidant Proteins by Incorporating Statistical Moments Based Features into Chou’s PseAAC. Journal of Theoretical Biology, 473, 1-8. https://doi.org/10.1016/j.jtbi.2019.04.019
Ahmad, J. and Hayat, M. (2019) MFSC: Multi-Voting Based Feature Selection for Classification of Golgi Proteins by Adopting the General Form of Chou’s PseAAC Components. Journal of Theoretical Biology, 463, 99-109. https://doi.org/10.1016/j.jtbi.2018.12.017
Zhang, S. and Liang, Y. (2018) Predicting Apoptosis Protein Subcellular Localization by Integrating Auto-Cross Correlation and PSSM into Chou’s PseAAC. Journal of Theoretical Biology, 457, 163-169. https://doi.org/10.1016/j.jtbi.2018.08.042
Mousavizadegan, M. and Mohabatkar, H. (2018) Computational Prediction of Antifungal Peptides via Chou’s PseAAC and SVM. Journal of Bioinformatics and Computational Biology, 16, Article ID: 1850016. https://doi.org/10.1142/S0219720018500166
Javed, F. and Hayat M. (2019) Predicting Subcellular Localizations of Multi-Label Proteins by Incorporating the Sequence Features into Chou’s PseAAC. Genomics, 111, 1325-1332. https://doi.org/10.1016/j.ygeno.2018.09.004
Fu, X., Zhu, W., Liso, B., Cai, L., Peng, L. and Yang, J. (2018) Improved DNA-Binding Protein Identification by Incorporating Evolutionary Information into the Chou’s PseAAC. IEEE Access, 6, 66545-66556.
Contreras-Torres, E. (2018) Predicting Structural Classes of Proteins by Incorporating Their Global and Local Physicochemical and Conformational Properties into General Chou’s PseAAC. Journal of Theoretical Biology, 454, 139-145. https://doi.org/10.1016/j.jtbi.2018.05.033
Akbar, S. and Hayat, M. (2018) iMethyl-STTNC: Identification of N6-Methyladenosine Sites by Extending the Idea of SAAC into Chou’s PseAAC to Formulate RNA Sequences. Journal of Theoretical Biology, 455, 205-211. https://doi.org/10.1016/j.jtbi.2018.07.018
Ahmad, J. and Hayat, M. (2018) MFSC: Multi-Voting Based Feature Selection for Classification of Golgi Proteins by Adopting the General form of Chou’s PseAAC Components. Journal of Theoretical Biology, 463, 99-109. https://doi.org/10.1016/j.jtbi.2018.12.017
Yu, B., Li, S., Qiu, W.Y., Chen, C., Chen, R.X., Wang, L., Wang, M.H. and Zhang, Y. (2017) Accurate Prediction of Subcellular Location of Apoptosis Proteins Combining Chou’s PseAAC and PsePSSM Based on Wavelet Denoising. Oncotarget, 8, 107640-107665. https://doi.org/10.18632/oncotarget.22585
Ju, Z. and He, J.J. (2017) Prediction of Lysine Propionylation Sites Using Biased SVM and Incorporating Four Different Sequence Features into Chou’s PseAAC. Journal of Molecular Graphics and Modelling, 76, 356-363. https://doi.org/10.1016/j.jmgm.2017.07.022
Tahir, M. and Hayat, M. (2016) iNuc-STNC: A Sequence-Based Predictor for Identification of Nucleosome Positioning in Genomes by Extending the Concept of SAAC and Chou’s PseAAC. Molecular BioSystems, 12, 2587-2593. https://doi.org/10.1039/C6MB00221H
Kabir, M. and Hayat, M. (2016) iRSpot-GAEnsC: Identifying Recombination Spots via Ensemble Classifier and Extending the Concept of Chou’s PseAAC to Formulate DNA Samples. Molecular Genetics and Genomics, 291, 285-296. https://doi.org/10.1007/s00438-015-1108-5
Sanchez, V., Peinado, A.M., Perez-Cordoba, J.L. and Gomez, A.M. (2015) A New Signal Characterization and Signal-Based Chou’s PseAAC Representation of Protein Sequences. Journal of Bioinformatics and Computational Biology, 13, Article ID: 1550024. https://doi.org/10.1142/S0219720015500249
Liu, B., Xu, J., Fan, S., Xu, R., Zhou, J. and Wang, X. (2015) PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation. Molecular Informatics, 34, 8-17. https://doi.org/10.1002/minf.201400025
Zhang, J., Zhao, X., Sun, P. and Ma, Z. (2014) PSNO: Predicting Cysteine S-Nitrosylation Sites by Incorporating Various Sequence-Derived Features into the General Form of Chou’s PseAAC. International Journal of Molecular Sciences, 15, 11204-11219. https://doi.org/10.3390/ijms150711204
Li, L., Yu, S., Xiao, W., Li, Y., Li, M., Huang, L., Zheng, X., Zhou, S. and Yang, H. (2014) Prediction of Bacterial Protein Subcellular Localization by Incorporating Various Features into Chou’s PseAAC and a Backward Feature Selection Approach. Biochimie, 104, 100-107. https://doi.org/10.1016/j.biochi.2014.06.001
Han, G.S., Yu, Z.G. and Anh, V. (2014) 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. Journal of Theoretical Biology, 344, 31-39. https://doi.org/10.1016/j.jtbi.2013.11.017
Xie, H.L., Fu, L. and Nie, X.D. (2013) Using Ensemble SVM to Identify Human GPCRs N-Linked Glycosylation Sites Based on the General Form of Chou’s PseAAC. Protein Engineering, Design and Selection, 26, 735-742. https://doi.org/10.1093/protein/gzt042
Pacharawongsakda, E. and Theeramunkong, T. (2013) 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, 311-320. https://doi.org/10.1109/TNB.2013.2272014
Fan, G.-L., Li, Q.-Z. and Zuo, Y.-C. (2013) Predicting Acidic and Alkaline Enzymes by Incorporating the Average Chemical Shift and Gene Ontology Informations into the General Form of Chou’s PseAAC. Process Biochemistry, 48, 1048-1053. https://doi.org/10.1016/j.procbio.2013.05.012
Chang, T.H., Wu, L.C., Lee, T.Y., Chen, S.P., Huang, H.D. and Horng, J.T. (2013) EuLoc: A Web-Server for Accurately Predict Protein Subcellular Localization in Eukaryotes by Incorporating Various Features of Sequence Segments into the General Form of Chou’s PseAAC. Journal of Computer-Aided Molecular Design, 27, 91-103. https://doi.org/10.1007/s10822-012-9628-0
Cao, D.S., Xu, Q.S. and Liang, Y.Z. (2013) propy: A Tool to Generate Various Modes of Chou’s PseAAC. Bioinformatics, 29, 960-962. https://doi.org/10.1093/bioinformatics/btt072
Sun, X.Y., Shi, S.P., Qiu, J.D., Suo, S.B., Huang, S.Y. and Liang, R.P. (2012) Identifying Protein Quaternary Structural Attributes by Incorporating Physicochemical Properties into the General Form of Chou’s PseAAC via Discrete Wavelet Transform. Molecular BioSystems, 8, 3178-3184. https://doi.org/10.1039/c2mb25280e
Qin, Y.F., Wang, C.H., Yu, X.Q., Zhu, J., Liu, T.G. and Zheng, X.Q. (2012) Predicting Protein Structural Class by Incorporating Patterns of Over-Represented k-mers into the General form of Chou’s PseAAC. Protein & Peptide Letters, 19, 388-397. https://doi.org/10.2174/092986612799789350
Mei, S. (2012) Predicting Plant Protein Subcellular Multi-Localization by Chou’s PseAAC Formulation Based Multi-Label Homolog Knowledge Transfer Learning. Journal of Theoretical Biology, 310, 80-87. https://doi.org/10.1016/j.jtbi.2012.06.028
Mei, S. (2012) Multi-Kernel Transfer Learning Based on Chou’s PseAAC Formulation for Protein Submitochondria Localization. Journal of Theoretical Biology, 293, 121-130. https://doi.org/10.1016/j.jtbi.2011.10.015
Liao, B., Xiang, Q. and Li, D. (2012) Incorporating Secondary Features into the General form of Chou’s PseAAC for Predicting Protein Structural Class. Protein & Peptide Letters, 19, 1133-1138. https://doi.org/10.2174/092986612803217051
Hayat, M. and Khan, A. (2012) Discriminating Outer Membrane Proteins with Fuzzy K-Nearest Neighbor Algorithms Based on the General Form of Chou’s PseAAC. Protein & Peptide Letters, 19, 411-421. https://doi.org/10.2174/092986612799789387
Wang, L., Zhang, R. and Mu, Y. (2019) Fu-SulfPred: Identification of Protein S-Sulfenylation Sites by Fusing Forests via Chou’s General PseAAC. Journal of Theoretical Biology, 461, 51-58. https://doi.org/10.1016/j.jtbi.2018.10.046
Shen, Y., Tang, J. and Guo, F. (2019) Identification of Protein Subcellular Localization via Integrating Evolutionary and Physicochemical Information into Chou’s General PseAAC. Journal of Theoretical Biology, 462, 230-239. https://doi.org/10.1016/j.jtbi.2018.11.012
Ilyas, S., Hussain, W., Ashraf, A., Khan, Y.D., Khan, S.A. and Chou, K.C. (2019) iMethylK-PseAAC: Improving Accuracy for Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-Steps Rule. Current Genomics, 20, 275-292. https://doi.org/10.2174/1389202920666190809095206
Chen, G., Cao, M., Yu, J., Guo, X. and Shi, S. (2019) Prediction and Functional Analysis of Prokaryote Lysine Acetylation Site by Incorporating Six Types of Features into Chou’s General PseAAC. Journal of Theoretical Biology, 461, 92-101. https://doi.org/10.1016/j.jtbi.2018.10.047
Adilina, S., Farid, D.M. and Shatabda, S. (2019) Effective DNA Binding Protein Prediction by Using Key Features via Chou’s General PseAAC. Journal of Theoretical Biology, 460, 64-78. https://doi.org/10.1016/j.jtbi.2018.10.027
Zhang, S. and Duan, X. (2018) Prediction of Protein Subcellular Localization with Oversampling Approach and Chou’s General PseAAC. Journal of Theoretical Biology, 437, 239-250. https://doi.org/10.1016/j.jtbi.2017.10.030
Srivastava, A., Kumar, R. and Kumar, M. (2018) BlaPred: Predicting and Classifying Beta-Lactamase Using a 3-Tier Prediction System via Chou’s General PseAAC. Journal of Theoretical Biology, 457, 29-36. https://doi.org/10.1016/j.jtbi.2018.08.030
Sankari, E.S. and Manimegalai, D.D. (2018) Predicting Membrane Protein Types by Incorporating a Novel Feature Set into Chou’s General PseAAC. Journal of Theoretical Biology, 455, 319-328. https://doi.org/10.1016/j.jtbi.2018.07.032
Rahman, S.M., Shatabda, S., Saha, S., Kaykobad, M. and Sohel Rahman, M. (2018) DPP-PseAAC: A DNA-Binding Protein Prediction Model Using Chou’s General PseAAC. Journal of Theoretical Biology, 452, 22-34. https://doi.org/10.1016/j.jtbi.2018.05.006
Liang, Y. and Zhang, S. (2018) Identify Gram-Negative Bacterial Secreted Protein Types by Incorporating Different Modes of PSSM into Chou’s General PseAAC via Kullback-Leibler Divergence. Journal of Theoretical Biology, 454, 22-29. https://doi.org/10.1016/j.jtbi.2018.05.035
Krishnan, M.S. (2018) Using Chou’s General PseAAC to Analyze the Evolutionary Relationship of Receptor Associated Proteins (RAP) with Various Folding Patterns of Protein Domains. Journal of Theoretical Biology, 445, 62-74. https://doi.org/10.1016/j.jtbi.2018.02.008
Ghauri, A.W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2018) pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou’s General PseAAC. Current Pharmaceutical Design, 24, 4034-4043. https://doi.org/10.2174/1381612825666181127101039
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset. Current Pharmaceutical Design, 24, 4013-4022. https://doi.org/10.2174/1381612824666181119145030
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mGneg: Predict Subcellular Localization of Gram-Negative Bacterial Proteins by Quasi-Balancing Training Dataset and General PseAAC. Journal of Theoretical Biology, 458, 92-102. https://doi.org/10.1016/j.jtbi.2018.09.005
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mHum: Predict Subcellular Localization of Multi-Location Human Proteins via General PseAAC to Winnow out the Crucial GO Information. Bioinformatics, 34, 1448- 1456. https://doi.org/10.1093/bioinformatics/btx711
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mGneg: Predict Subcellular Localization of Gram-Negative Bacterial Proteins by Deep Gene Ontology Learning via General PseAAC. Genomics, 110, 231-239. https://doi.org/10.1016/j.ygeno.2017.10.002
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mEuk: Predict Subcellular Localization of Multi-Label Eukaryotic Proteins by Extracting the Key GO Information into General PseAAC. Genomics, 110, 50-58. https://doi.org/10.1016/j.ygeno.2017.08.005
Butt, A.H., Rasool, N. and Khan, Y.D. (2018) Predicting Membrane Proteins and Their Types by Extracting Various Sequence Features into Chou’s General PseAAC. Molecular Biology Reports, 45, 2295-2306. https://doi.org/10.1007/s11033-018-4391-5
Xu, C., Ge, L., Zhang, Y., Dehmer, M. and Gutman, I. (2017) Computational Prediction of Therapeutic Peptides Based on Graph Index. Journal of Biomedical Informatics, 75, 63-69. https://doi.org/10.1016/j.jbi.2017.09.011
Xiao, X., Cheng, X., Su, S., Nao, Q. and Chou, K.C. (2017) pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins. Natural Science, 9, 331-349. https://doi.org/10.4236/ns.2017.99032
Qiu, W.R., Zheng, Q.S., Sun, B.Q. and Xiao, X. (2017) Multi-iPPseEvo: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou’s General PseAAC via Grey System Theory. Molecular Informatics, 36, UNSP 1600085. https://doi.org/10.1002/minf.201600085
Qiu, W.R., Sun, B.Q., Xiao, X., Xu, D. and Chou, K.C. (2017) iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory. Molecular Informatics, 36, UNSP 1600010. https://doi.org/10.1002/minf.201600010
Meher, P.K., Sahu, T.K., Saini, V. and Rao, A.R. (2017) Predicting Antimicrobial Peptides with Improved Accuracy by Incorporating the Compositional, Physico-Chemical and Structural Features into Chou’s General PseAAC. Scientific Reports, 7, Article No. 42362. https://doi.org/10.1038/srep42362
Liu, L.M., Xu, Y. and Chou, K.C. (2017) iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC. Medicinal Chemistry, 13, 552-559. https://doi.org/10.2174/1573406413666170515120507
Khan, M., Hayat, M., Khan, S.A. and Iqbal, N. (2017) Unb-DPC: Identify Mycobacterial Membrane Protein Types by Incorporating Un-Biased Dipeptide Composition into Chou’s General PseAAC. Journal of Theoretical Biology, 415, 13-19. https://doi.org/10.1016/j.jtbi.2016.12.004
Ju, Z. and He, J.J. (2017) Prediction of Lysine Crotonylation Sites by Incorporating the Composition of k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Molecular Graphics and Modelling, 77, 200-204. https://doi.org/10.1016/j.jmgm.2017.08.020
Cheng, X., Xiao, X. and Chou, K.C. (2017) pLoc-mVirus: Predict Subcellular Localization of Multi-Location Virus Proteins via Incorporating the Optimal GO Information into General PseAAC. Gene, 628, 315-321. (Erratum: ibid., 2018, Vol. 644, 156-156) https://doi.org/10.1016/j.gene.2017.07.036
Cheng, X., Xiao, X. and Chou, K.C. (2017) pLoc-mPlant: Predict Subcellular Localization of Multi-Location Plant Proteins via Incorporating the Optimal GO Information into General PseAAC. Molecular BioSystems, 13, 1722-1727. https://doi.org/10.1039/C7MB00267J
Tiwari, A.K. (2016) Prediction of G-Protein Coupled Receptors and Their Subfamilies by Incorporating Various Sequence Features into Chou’s General PseAAC. Computer Methods and Programs in Biomedicine, 134, 197- 213. https://doi.org/10.1016/j.cmpb.2016.07.004
Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C. and Chou, K.C. (2016) iHyd-PseCp: Identify Hydroxyproline and Hydroxylysine in Proteins by Incorporating Sequence-Coupled Effects into General PseAAC. Oncotarget, 7, 44310- 44321. https://doi.org/10.18632/oncotarget.10027
Ju, Z., Cao, J.Z. and Gu, H. (2016) Predicting Lysine Phosphoglycerylation with Fuzzy SVM by Incorporating k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Theoretical Biology, 397, 145-150. https://doi.org/10.1016/j.jtbi.2016.02.020
Jia, J., Zhang, L., Liu, Z., Xiao, X. and Chou, K.C. (2016) pSumo-CD: Predicting Sumoylation Sites in Proteins with Covariance Discriminant Algorithm by Incorporating Sequence-Coupled Effects into General PseAAC. Bioinformatics, 32, 3133-3141. https://doi.org/10.1093/bioinformatics/btw387
Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iCar-PseCp: Identify Carbonylation Sites in Proteins by Monto Carlo Sampling and Incorporating Sequence Coupled Effects into General PseAAC. Oncotarget, 7, 34558-34570. https://doi.org/10.18632/oncotarget.9148
Zhang, S.L. (2015) Accurate Prediction of Protein Structural Classes by Incorporating PSSS and PSSM into Chou’s General PseAAC. Chemometrics and Intelligent Laboratory Systems, 142, 28-35. https://doi.org/10.1016/j.chemolab.2015.01.004
Zhang, M., Zhao, B. and Liu, X. (2015) Predicting Industrial Polymer Melt Index via Incorporating Chaotic Characters into Chou’s General PseAAC. Chemometrics and Intelligent Laboratory Systems, 146, 232-240. https://doi.org/10.1016/j.chemolab.2015.05.028
Sharma, R., Dehzangi, A., Lyons, J., Paliwal, K., Tsunoda, T. and Sharma, A. (2015) Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou’s General PseAAC. IEEE Transactions on NanoBioscience, 14, 915-926. https://doi.org/10.1109/TNB.2015.2500186
Ju, Z., Cao, J.Z. and Gu, H. (2015) 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. Journal of Theoretical Biology, 385, 50-57. https://doi.org/10.1016/j.jtbi.2015.07.030
Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K. and Sattar, A. (2015) Gram-Positive and Gram- Negative Protein Subcellular Localization by Incorporating Evolutionary-Based Descriptors into Chou’s General PseAAC. Journal of Theoretical Biology, 364, 284-294. https://doi.org/10.1016/j.jtbi.2014.09.029
Ahmad, S., Kabir, M. and Hayat, M. (2015) Identification of Heat Shock Protein families and J-Protein Types by Incorporating Dipeptide Composition into Chou’s General PseAAC. Computer Methods and Programs in Biomedicine, 122, 165-174. https://doi.org/10.1016/j.cmpb.2015.07.005
Hayat, M. and Iqbal, N. (2014) Discriminating Protein Structure Classes by Incorporating Pseudo Average Chemical Shift to Chou’s General PseAAC and Support Vector Machine. Computer Methods and Programs in Biomedicine, 116, 184-192. https://doi.org/10.1016/j.cmpb.2014.06.007
Chou, K.C. (2009) Pseudo Amino Acid Composition and Its Applications in Bioinformatics, Proteomics and System Biology. Current Proteomics, 6, 262-274. https://doi.org/10.2174/157016409789973707
Chen, W., Lei, T.Y., Jin, D.C., Lin, H. and Chou, K.C. (2014) PseKNC: A Flexible Web-Server for Generating Pseudo K-Tuple Nucleotide Composition. Analytical Biochemistry, 456, 53-60. https://doi.org/10.1016/j.ab.2014.04.001
Chen, W., Lin, H. and Chou, K.C. (2015) Pseudo Nucleotide Composition or PseKNC: An Effective Formulation for Analyzing Genomic Sequences. Molecular BioSystems, 11, 2620-2634. https://doi.org/10.1039/C5MB00155B
Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2014) iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition. Biomed Research International, 2014, Article ID: 623149. https://doi.org/10.1155/2014/623149
Chen, W., Tang, H., Ye, J., Lin, H. and Chou, K.C. (2016) iRNA-PseU: Identifying RNA Pseudouridine Sites. Molecular Therapy—Nucleic Acids, 5, e332.
Liu, B., Fang, L., Long, R., Lan, X. and Chou, K.C. (2016) iEnhancer-2L: A Two-Layer Predictor for Identifying Enhancers and Their Strength by Pseudo k-Tuple Nucleotide Composition. Bioinformatics, 32, 362-369. https://doi.org/10.1093/bioinformatics/btv604
Liu, B., Long, R. and Chou, K.C. (2016) iDHS-EL: Identifying DNase I Hypersensi-Tivesites by Fusing Three Different Modes of Pseudo Nucleotide Composition into an Ensemble Learning Framework. Bioinformatics, 32, 2411-2418. https://doi.org/10.1093/bioinformatics/btw186
Feng, P., Ding, H., Yang, H., Chen, W., Lin, H. and Chou, K.C. (2017) iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. Molecular Therapy—Nucleic Acids, 7, 155-163. https://doi.org/10.1016/j.omtn.2017.03.006
Liu, B., Wang, S., Long, R. and Chou, K.C. (2017) iRSpot-EL: Identify Recombination Spots with an Ensemble Learning Approach. Bioinformatics, 33, 35-41. https://doi.org/10.1093/bioinformatics/btw539
Liu, B., Yang, F. and Chou, K.C. (2017) 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Inter- acting RNAs and Their Function. Molecular Therapy—Nucleic Acids, 7, 267-277. https://doi.org/10.1016/j.omtn.2017.04.008
Sabooh, M.F., Iqbal, N., Khan, M., Khan, M. and Maqbool, H.F. (2018) Identifying 5-Methylcytosine Sites in RNA Sequence Using Composite Encoding Feature into Chou’s PseKNC. Journal of Theoretical Biology, 452, 1-9. https://doi.org/10.1016/j.jtbi.2018.04.037
Chou, K.C. (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems, 9, 1092-1100. https://doi.org/10.1039/c3mb25555g
Cheng, X., Zhao, S.G., Xiao, X. and Chou, K.C. (2017) iATC-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals. Bioinformatics, 33, 341-346. (Corrigendum, ibid., 2017, Vol. 33, 2610) https://doi.org/10.1093/bioinformatics/btx387
Cheng, X., Zhao, S.G., Xiao, X. and Chou, K.C. (2017) iATC-mHyb: A Hybrid Multi-Label Classifier for Predicting the Classification of Anatomical Therapeutic Chemicals. Oncotarget, 8, 58494-58503. https://doi.org/10.18632/oncotarget.17028
Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2013) iRSpot-PseDNC: Identify Recombination Spots with Pseudo Dinucleotide Composition. Nucleic Acids Research, 41, e68. https://doi.org/10.1093/nar/gks1450
Chou, K.C. (2001) Using Subsite Coupling to Predict Signal Peptides. Protein Engineering, Design and Selection, 14, 75-79. https://doi.org/10.1093/protein/14.2.75
Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) Identification of Protein-Protein Binding Sites by Incorporating the Physicochemical Properties and Stationary Wavelet Transforms into Pseudo Amino Acid Composition (iPPBS-PseAAC). Journal of Biomolecular Structure and Dynamics, 34, 1946-1961. https://doi.org/10.1080/07391102.2015.1095116
Chou, K.C. and Shen, H.B. (2009) Recent Advances in Developing Web-Servers for Predicting Protein Attributes. Natural Science, 1, 63-92. https://doi.org/10.4236/ns.2009.12011
Song, J., Li, F., Leier, A., Marquez-Lago, T.T., Akutsu, T., Haffari, G., Chou, K.C., Webb, G.I. and Pike, R.N. (2018) PROSPERous: High-Throughput Prediction of Substrate Cleavage Sites for 90 Proteases with Improved Accuracy. Bioinformatics, 34, 684-687. https://doi.org/10.1093/bioinformatics/btx670
Yang, H., Qiu, W.R., Liu, G., Guo, F.B., Chen, W., Chou, K.C. and Lin, H. (2018) iRSpot-Pse6NC: Identifying Recombination Spots in Saccharomyces cerevisiae by Incorporating Hexamer Composition into General PseKNC. International Journal of Biological Sciences, 14, 883-891. https://doi.org/10.7150/ijbs.24616
Xu, Y., Li, C. and Chou, K.C. (2017) iPreny-PseAAC: Identify C-Terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC. Medicinal Chemistry, 13, 544-551. https://doi.org/10.2174/1573406413666170419150052
Xu ,Y. and Chou, K.C. (2016) Recent Progress in Predicting Posttranslational Modification Sites in Proteins. Current Topics in Medicinal Chemistry, 16, 591-603. https://doi.org/10.2174/1568026615666150819110421
Xu, Y., Wen, X., Wen, L.S., Wu, L.Y., Deng, N.Y. and Chou, K.C. (2014) iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition. PLoS ONE, 9, e105018. https://doi.org/10.1371/journal.pone.0105018
Xu, Y., Wen, X., Shao, X.J., Deng, N.Y. and Chou, K.C. (2014) iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition. International Journal of Molecular Sciences, 15, 7594-7610. https://doi.org/10.3390/ijms15057594
Xu, Y., Shao, X.J., Wu, L.Y., Deng, N.Y. and Chou, K.C. (2013) iSNO-AAPair: Incorporating Amino Acid Pairwise Coupling into PseAAC for Predicting Cysteine S-Nitrosylation Sites in Proteins. PeerJ, 1, e171. https://doi.org/10.7717/peerj.171
Xu, Y., Ding, J., Wu, L.Y. and Chou, K.C. (2013) iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition. PLoS ONE, 8, e55844. https://doi.org/10.1371/journal.pone.0055844
Xu, Y., Ding, J., Huang, Q. and Deng, N.Y. (2013) Prediction of Protein Methylation Sites Using Conditional Random Field. Protein & Peptide Letters, 20, 71-77. https://doi.org/10.2174/092986613804096865
Jia, J., Liu, Z., Xiao, X. and Chou, K.C. (2015) iPPI-Esml: An Ensemble Classifier for Identifying the Interactions of Proteins by Incorporating Their Physicochemical Properties and Wavelet Transforms into PseAAC. Journal of Theoretical Biology, 377, 47-56. https://doi.org/10.1016/j.jtbi.2015.04.011
Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iSuc-PseOpt: Identifying Lysine Succinylation Sites in Proteins by Incorporating Sequence-Coupling Effects into Pseudo Components and Optimizing Imbalanced Training Dataset. Analytical Biochemistry, 497, 48-56. https://doi.org/10.1016/j.ab.2015.12.009
Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) pSuc-Lys: Predict Lysine Succinylation Sites in Proteins with PseAAC and Ensemble Random Forest Approach. Journal of Theoretical Biology, 394, 223-230. https://doi.org/10.1016/j.jtbi.2016.01.020
Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules, 21, 95. https://doi.org/10.3390/molecules21010095
Jia, J., Li, X., Qiu, W., Xiao, X. and Chou, K.C. (2019) iPPI-PseAAC(CGR): Identify Protein-Protein Interactions by Incorporating Chaos Game Representation into PseAAC. Journal of Theoretical Biology, 460, 195-203. https://doi.org/10.1016/j.jtbi.2018.10.021
Liu, B., Xu, J., Lan, X., Xu, R., Zhou, J., Wang, X. and Chou, K.C. (2014) 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, e106691. https://doi.org/10.1371/journal.pone.0106691
Liu, B., Zhang, D., Xu, R., Xu, J., Wang, X., Chen, Q., Dong, Q. and Chou, K.C. (2014) Combining Evolutionary Information Extracted from Frequency Profiles with Sequence-Based Kernels for Protein Remote Homology Detection. Bioinformatics, 30, 472-479. https://doi.org/10.1093/bioinformatics/btt709
Liu, B., Fang, L., Liu, F., Wang, X., Chen, J. and Chou, K.C. (2015) Identification of Real microRNA Precursors with a Pseudo Structure Status Composition Approach. PLoS ONE, 10, e0121501. https://doi.org/10.1371/journal.pone.0121501
Liu, B., Fang, L., Wang, S., Wang, X., Li, H. and Chou, K.C. (2015) Identification of microRNA Precursor with the Degenerate K-Tuple or Kmer Strategy. Journal of Theoretical Biology, 385, 153-159. https://doi.org/10.1016/j.jtbi.2015.08.025
Liu, B., Liu, F., Fang, L., Wang, X. and Chou, K.C. (2015) repDNA: A Python Package to Generate Various Modes of Feature Vectors for DNA Sequences by Incorporating User-Defined Physicochemical Properties and Sequence-Order Effects. Bioinformatics, 31, 1307-1309. https://doi.org/10.1093/bioinformatics/btu820
Liu, B., Liu, F., Wang, X., Chen, J., Fang, L. and Chou, K.C. (2015) Pse-in-One: A Web Server for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. Nucleic Acids Research, 43, W65- W71. https://doi.org/10.1093/nar/gkv458
Liu, B., Fang, L., Liu, F., Wang, X. and Chou, K.C. (2016) iMiRNA-PseDPC: microRNA Precursor Identification with a Pseudo Distance-Pair Composition Approach. Journal of Biomolecular Structure and Dynamics, 34, 223- 235. https://doi.org/10.1080/07391102.2015.1014422
Liu, B., Liu, F., Fang, L., Wang, X. and Chou, K.C. (2016) repRNA: A Web Server for Generating Various Feature Vectors of RNA Sequences. Molecular Genetics and Genomics, 291, 473-481. https://doi.org/10.1007/s00438-015-1078-7
Liu, B., Wu, H. and Chou, K.C. (2017) Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. Natural Science, 9, 67-91. https://doi.org/10.4236/ns.2017.94007
Liu, B., Wu, H., Zhang, D., Wang, X. and Chou, K.C. (2017) Pse-Analysis: A Python Package for DNA/RNA and Protein/Peptide Sequence Analysis Based on Pseudo Components and Kernel Methods. Oncotarget, 8, 13338- 13343. https://doi.org/10.18632/oncotarget.14524
Liu, B. (2019) BioSeq-Analysis: A Platform for DNA, RNA, and Protein Sequence Analysis Based on Machine Learning Approaches. Briefings in Bioinformatics, 20, 1280-1294. https://doi.org/10.1093/bib/bbx165
Liu, B., Li, K., Huang, D.S. and Chou, K.C. (2018) iEnhancer-EL: Identifying Enhancers and Their Strength with Ensemble Learning Approach. Bioinformatics, 34, 3835-3842. https://doi.org/10.1093/bioinformatics/bty458
Liu, B., Weng, F., Huang, D.S. and Chou, K.C. (2018) iRO-3wPseKNC: Identify DNA Replication Origins by Three-Window-Based PseKNC. Bioinformatics, 34, 3086-3093. https://doi.org/10.1093/bioinformatics/bty312
Liu, B., Yang, F., Huang, D.S. and Chou, K.C. (2018) iPromoter-2L: A Two-Layer Predictor for Identifying Promoters and Their Types by Multi-Window-Based PseKNC. Bioinformatics, 34, 33-40. https://doi.org/10.1093/bioinformatics/btx579
Liu, B., Gao, X. and Zhang, H. (2019) BioSeq-Analysis2.0: An Updated Platform for Analyzing DNA, RNA and Protein Sequences at Sequence Level and Residue Level Based on Machine Learning Approaches. Nucleic Acids Research, 47, e127. https://doi.org/10.1093/nar/gkz740
Chen, W., Lin, H., Feng, P.M., Ding, C., Zuo, Y.C. and Chou, K.C. (2012) iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE, 7, e47843. https://doi.org/10.1371/journal.pone.0047843
Chen, W., Feng, P.M., Deng, E.Z., Lin, H. and Chou, K.C. (2014) iTIS-PseTNC: A Sequence-Based Predictor for Identifying Translation Initiation Site in Human Genes Using Pseudo Trinucleotide Composition. Analytical Biochemistry, 462, 76-83. https://doi.org/10.1016/j.ab.2014.06.022
Chen, W., Zhang, X., Brooker, J., Lin, H., Zhang, L. and Chou, K.C. (2015) PseKNC-General: A Cross-Platform Package for Generating Various Modes of Pseudo Nucleotide Compositions. Bioinformatics, 31, 119-120. https://doi.org/10.1093/bioinformatics/btu602
Chen, W., Ding, H., Feng, P., Lin, H. and Chou, K.C. (2016) iACP: A Sequence-Based Tool for Identifying Anticancer Peptides. Oncotarget, 7, 16895-16909. https://doi.org/10.18632/oncotarget.7815
Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2016) Using Deformation Energy to Analyze Nucleosome Positioning in Genomes. Genomics, 107, 69-75. https://doi.org/10.1016/j.ygeno.2015.12.005
Chen, W., Feng, P., Yang, H., Ding, H., Lin, H. and Chou, K.C. (2017) iRNA-AI: Identifying the Adenosine to Inosine Editing Sites in RNA Sequences. Oncotarget, 8, 4208-4217. https://doi.org/10.18632/oncotarget.13758
Lin, H. and Li, Q.Z. (2007) Predicting Conotoxin Superfamily and Family by Using Pseudo Amino Acid Composition and Modified Mahalanobis Discriminant. Biochemical and Biophysical Research Communications, 354, 548-551. https://doi.org/10.1016/j.bbrc.2007.01.011
Lin, H. and Li, Q.Z. (2007) Using Pseudo Amino Acid Composition to Predict Protein Structural Class: Approached by Incorporating 400 Dipeptide Components. Journal of Computational Chemistry, 28, 1463-1466. https://doi.org/10.1002/jcc.20554
Lin, H. and Ding, H. (2011) Predicting Ion Channels and Their Types by the Dipeptide Mode of Pseudo Amino Acid Composition. Journal of Theoretical Biology, 269, 64-69. https://doi.org/10.1016/j.jtbi.2010.10.019
Lin, H., Chen, W. and Ding, H. (2013) AcalPred: A Sequence-Based Tool for Discriminating between Acidic and Alkaline Enzymes. PLoS ONE, 8, e75726. https://doi.org/10.1371/journal.pone.0075726
Lin, H., Ding, C., Yuan, L.F., Chen, W., Ding, H., Li, Z.Q., Guo, F.B., Hung, J. and Rao, N.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 Biomathematics, 6, Article No. 1350003. https://doi.org/10.1142/S1793524513500034
Lin, H., Deng, E.Z., Ding, H., Chen, W. and Chou, K.C. (2014) iPro54-PseKNC: A Sequence-Based Predictor for Identifying Sigma-54 Promoters in Prokaryote with Pseudo k-Tuple Nucleotide Composition. Nucleic Acids Research, 42, 12961-12972. https://doi.org/10.1093/nar/gku1019
Shao, Y.T. and Chou, K.C. (2020) pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins. Natural Science, 12, 281-291. https://doi.org/10.4236/ns.2020.125024
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mAnimal Is Working. American Journal of Virology & Disease, 2, 1-2.
Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning. Natural Science, 12, 237-247. https://doi.org/10.4236/ns.2020.125021
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mPlant Is Working. Growth Journal, 1, 1-2.
Lu, Z. and Chou, K.C. (2020) iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning. Advances in Bioscience and Biotechnology, 11, 153-159. https://doi.org/10.4236/abb.2020.115012
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iATC_Deep-mISF Is Working. Global Journal of Science Frontier Research: G Bio-Tech & Genetics, 20, 1-3.
Liu, X.X. and Chou, K.C. (2020) pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning. Advances in Bioscience and Biotechnology, 11, 141-152. https://doi.org/10.4236/abb.2020.115011
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mGneg Is Working. Clin Med Case Rep J, 1, 1-2.
Shao, Y.T., Cheng, X. and Chou, K.C. (2020) pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning. Molecular Therapy—Nucleic Acids (MTNA), in press.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mVirus Is Working. Clinical Research and Trials, 6, 1-2.
Lu, Z. and Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mGpos Is Working. Journal of Biomedical Science and Engineering, 13, 55-65. https://doi.org/10.4236/jbise.2020.135005
Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_Deep-mGpos Is Working. Open Acc J Bio Sci, 2, 345-346.
Chou, K.C. (2020) How the Artificial Intelligence Tool iSuc-PseOpt Is Working for Predicting Lysine Succinylation Sites in Proteins. Biomedical Research and Clinical Reviews, 1, 1-2.
Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mHum: Predict Subcellular Localization of Human Proteins by PseAAC and Quasi-Balancing Training Dataset. Genomics, 111, 1274-1282. https://doi.org/10.1016/j.ygeno.2018.08.007
Chou, K.C. (2017) An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science. Current Topics in Medicinal Chemistry, 17, 2337-2358. https://doi.org/10.2174/1568026617666170414145508