Recently, a very useful method called “pLoc_Deep-mHum” has been proposed for finding against the Pandemic COVID-19. Illustrated in this short report is a step-by-step guide for how to use its web-server.
Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. Natural Science, 1-31, Vol.12, No.7, 526-551.
Chou, K.C. (2019) Advance in Predicting Subcellular Localization of Multi-Label Proteins and Its Implication for Developing Multi-Target Drugs. Current Medicinal Chemistry, 26, 4918-4943. https://doi.org/10.2174/0929867326666190507082559
Maxwell, A., Li, R., Yang, B., Weng, H., Ou, A., Hong, H., Zhou, Z., Gong, P. and Zhang, C. (2017) Deep Learning Architectures for Multi-Label Classification of Intelligent Health Risk Prediction. BMC Bioinformatics, 18, Article No. 523. https://doi.org/10.1186/s12859-017-1898-z
Khan, S., Khan, M., Iqbal, N., Hussain, T., Khan, S.A. and Chou, K.C. (2020) A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule. International Journal of Peptide Research and Therapeutics, 26, 795-809. https://doi.org/10.1007/s10989-019-09887-3
Nazari, I., Tahir, M., Tayari, H. and Chong, K.T. (2019) iN6-Methyl (5-Step): Identifying RNA N6-Methyladenosine Sites Using Deep Learning Mode via Chou’s 5-Step Rules and Chou’s General PseKNC. Chemometrics and Intelligent Laboratory Systems, 193, Article ID: 103811. https://doi.org/10.1016/j.chemolab.2019.103811
Khan, Z.U., Ali, F., Khan, I.A., Hussain, Y. and Pi, D. (2019) iRSpot-SPI: Deep Learning-Based Recombination Spots Prediction by Incorporating Secondary Sequence Information Coupled with Physio-Chemical Properties via Chou’s 5-Step Rule and Pseudo Components. Chemometrics and Intelligent Laboratory Systems, 189, 169-180. https://doi.org/10.1016/j.chemolab.2019.05.003
Chou, K.C. (2001) Prediction of Protein Cellular Attributes Using Pseudo Amino Acid Composition. Proteins: Structure, Function, and Genetics, 43, 246-255. (Erratum: ibid., 2001, Vol. 44, 60) https://doi.org/10.1002/prot.1035
Chou, K.C. (2005) Using Amphiphilic Pseudo Amino Acid Composition to Predict Enzyme Subfamily Classes. Bioinformatics, 21, 10-19. https://doi.org/10.1093/bioinformatics/bth466
Ding, Y.S. and Zhang, T.L. (2008) Using Chou’s Pseudo Amino Acid Composition to Predict Subcellular Localization of Apoptosis Proteins: An Approach with Immune Genetic Algorithm-Based Ensemble Classifier. Pattern Recognition Letters, 29, 1887-1892. https://doi.org/10.1016/j.patrec.2008.06.007
Fang, Y., Guo, Y., Feng, Y. and Li, M. (2008) Predicting DNA-Binding Proteins: Approached from Chou’s Pseudo Amino Acid Composition and Other Specific Sequence Features. Amino Acids, 34, 103-109. https://doi.org/10.1007/s00726-007-0568-2
Jiang, X., Wei, R., Zhang, T.L. and Gu, Q. (2008) Using the Concept of Chou’s Pseudo Amino Acid Composition to Predict Apoptosis Proteins Subcellular Location: An Approach by Approximate Entropy. Protein & Peptide Letters, 15, 392-396. https://doi.org/10.2174/092986608784246443
Jiang, X., Wei, R., Zhao, Y. and Zhang, T. (2008) Using Chou’s Pseudo Amino Acid Composition Based on Approximate Entropy and an Ensemble of AdaBoost Classifiers to Predict Protein Subnuclear Location. Amino Acids, 34, 669-675. https://doi.org/10.1007/s00726-008-0034-9
Li, F.M. and Li, Q.Z. (2008) Predicting Protein Subcellular Location Using Chou’s Pseudo Amino Acid Composition and Improved Hybrid Approach. Protein & Peptide Letters, 15, 612-616. https://doi.org/10.2174/092986608784966930
Lin, H. (2008) The Modified Mahalanobis Discriminant for Predicting Outer Membrane Proteins by Using Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 252, 350-356. https://doi.org/10.1016/j.jtbi.2008.02.004
Lin, H., Ding, H., Guo, F.B., Zhang, A.Y. and Huang, J. (2008) Predicting Subcellular Localization of Mycobacterial Proteins by Using Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 15, 739-744. https://doi.org/10.2174/092986608785133681
Nanni, L. and Lumini, A. (2008) Genetic Programming for Creating Chou’s Pseudo Amino Acid Based Features for Submitochondria Localization. Amino Acids, 34, 653-660. https://doi.org/10.1007/s00726-007-0018-1
Zhang, G.Y., Li, H.C., Gao, J.Q. and Fang, B.S. (2008) Predicting Lipase Types by Improved Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 15, 1132-1137. https://doi.org/10.2174/092986608786071184
Zhang, S.W., Chen, W., Yang, F. and Pan, Q. (2008) Using Chou’s Pseudo Amino Acid Composition to Predict Protein Quaternary Structure: A Sequence-Segmented PseAAC Approach. Amino Acids, 35, 591-598. https://doi.org/10.1007/s00726-008-0086-x
Zhang, S.W., Zhang, Y.L., Yang, H.F., Zhao, C.H. and Pan, Q. (2008) Using the Concept of Chou’s Pseudo Amino Acid Composition to Predict Protein Subcellular Localization: An Approach by Incorporating Evolutionary Information and von Neumann Entropies. Amino Acids, 34, 565-572. https://doi.org/10.1007/s00726-007-0010-9
Chen, C., Chen, L., Zou, X. and Cai, P. (2009) Prediction of Protein Secondary Structure Content by Using the Concept of Chou’s Pseudo Amino Acid Composition and Support Vector Machine. Protein & Peptide Letters, 16, 27-31. https://doi.org/10.2174/092986609787049420
Georgiou, D.N., Karakasidis, T.E., Nieto, J.J. and Torres, A. (2009) Use of Fuzzy Clustering Technique and Matrices to Classify Amino Acids and Its Impact to Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 257, 17-26. https://doi.org/10.1016/j.jtbi.2008.11.003
Li, Z.C., Zhou, X.B., Dai, Z. and Zou, X.Y. (2009) Prediction of Protein Structural Classes by Chou’s Pseudo Amino Acid Composition: Approached Using Continuous Wavelet Transform and Principal Component Analysis. Amino Acids, 37, 415-425. https://doi.org/10.1007/s00726-008-0170-2
Lin, H., Wang, H., Ding, H., Chen, Y.L. and Li, Q.Z. (2009) Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition. Acta Biotheoretica, 57, 321-330. https://doi.org/10.1007/s10441-008-9067-4
Qiu, J.D., Huang, J.H., Liang, R.P. and Lu, X.Q. (2009) Prediction of G-Protein-Coupled Receptor Classes Based on the Concept of Chou’s Pseudo Amino Acid Composition: An Approach from Discrete Wavelet Transform. Analytical Biochemistry, 390, 68-73. https://doi.org/10.1016/j.ab.2009.04.009
Zeng, Y.H., Guo, Y.Z., Xiao, R.Q., Yang, L., Yu, L.Z. and Li, M.L. (2009) Using the Augmented Chou’s Pseudo Amino Acid Composition for Predicting Protein Submitochondria Locations Based on Auto Covariance Approach. Journal of Theoretical Biology, 259, 366-372. https://doi.org/10.1016/j.jtbi.2009.03.028
Esmaeili, M., Mohabatkar, H. and Mohsenzadeh, S. (2010) Using the Concept of Chou’s Pseudo Amino Acid Composition for Risk Type Prediction of Human Papillomaviruses. Journal of Theoretical Biology, 263, 203-209. https://doi.org/10.1016/j.jtbi.2009.11.016
Gu, Q., Ding, Y.S. and Zhang, T.L. (2010) Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chou’s Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns. Protein & Peptide Letters, 17, 559-567. https://doi.org/10.2174/092986610791112693
Mohabatkar, H. (2010) Prediction of Cyclin Proteins Using Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 17, 1207-1214. https://doi.org/10.2174/092986610792231564
Qiu, J.D., Huang, J.H., Shi, S.P. and Liang, R.P. (2010) Using the Concept of Chou’s Pseudo Amino Acid Composition to Predict Enzyme Family Classes: An Approach with Support Vector Machine Based on Discrete Wavelet Transform. Protein & Peptide Letters, 17, 715-722. https://doi.org/10.2174/092986610791190372
Sahu, S.S. and Panda, G. (2010) A Novel Feature Representation Method Based on Chou’s Pseudo Amino Acid Composition for Protein Structural Class Prediction. Computational Biology and Chemistry, 34, 320-327. https://doi.org/10.1016/j.compbiolchem.2010.09.002
Yu, L., Guo, Y., Li, Y., Li, G., Li, M., Luo, J., Xiong, W. and Qin, W. (2010) SecretP: Identifying Bacterial Secreted Proteins by Fusing New Features into Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 267, 1-6. https://doi.org/10.1016/j.jtbi.2010.08.001
Guo, J., Rao, N., Liu, G., Yang, Y. and Wang, G. (2011) Predicting Protein Folding Rates Using the Concept of Chou’s Pseudo Amino Acid Composition. Journal of Computational Chemistry, 32, 1612-1617. https://doi.org/10.1002/jcc.21740
Lin, J. and Wang, Y. (2011) Using a Novel AdaBoost Algorithm and Chou’s Pseudo Amino Acid Composition for Predicting Protein Subcellular Localization. Protein & Peptide Letters, 18, 1219-1225. https://doi.org/10.2174/092986611797642797
Lin, J., Wang, Y. and Xu, X. (2011) A Novel Ensemble and Composite Approach for Classifying Proteins Based on Chou’s Pseudo Amino Acid Composition. African Journal of Biotechnology, 10, 16963-16968.
Mohabatkar, H., Mohammad Beigi, M. and Esmaeili, A. (2011) Prediction of GABAA Receptor Proteins Using the Concept of Chou’s Pseudo Amino Acid Composition and Support Vector Machine. Journal of Theoretical Biology, 281, 18-23. https://doi.org/10.1016/j.jtbi.2011.04.017
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 & 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 & 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., Li, N., Xia, J., Chen, D., Peng, Y., Xiao, Y., Wei, W., Wang, D. and Wang, 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 & 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, 365, 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. (2018) 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. (2019) 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 & 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. (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://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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Mandal, M., Mukhopadhyay, A. and Maulik, U. (2015) Prediction of Protein Subcellular Localization by Incorporating Multiobjective PSO-Based Feature Subset Selection into the General Form of Chou’s PseAAC. Medical & Biological Engineering & Computing, 53, 331-344. https://doi.org/10.1007/s11517-014-1238-7
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
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
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
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
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
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
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
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
Fu, X., Zhu, W., Liao, 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. https://doi.org/10.1109/ACCESS.2018.2876656
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
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
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
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
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
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
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
Chou, K.C. (2011) Some Remarks on Protein Attribute Prediction and Pseudo Amino Acid Composition (50th Anniversary Year Review, 5-Steps Rule). Journal of Theoretical Biology, 273, 236-247. https://doi.org/10.1016/j.jtbi.2010.12.024
Barukab, O., Khan, Y.D., Khan, S.A. and Chou, K.C. (2019) iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-Steps Rule and Pseudo Components. Current Genomics, 20, 306-320. https://doi.org/10.2174/1389202920666190819091609
Chen, Y. and Fan, X. (2019) Use Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangsheng Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 20, 220-230. https://doi.org/10.2174/1566524019666191011160543
Du, X., Diao, Y., Liu, H. and Li, S. (2019) MsDBP: Exploring DNA-Binding Proteins by Integrating Multi-Scale Sequence Information via Chou’s 5-Steps Rule. Journal of Proteome Research, 18, 3119-3132. https://doi.org/10.1021/acs.jproteome.9b00226
Dutta, A., Dalmia, A., A. R, Singh, K.K. and Anand, A. (2020) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computers in Biology and Medicine, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
Hussain, W., Khan, S.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPalmitoylC-PseAAC: A Sequence-Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Palmitoylation Sites in Proteins. Analytical Biochemistry, 568, 14-23. https://doi.org/10.1016/j.ab.2018.12.019
Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPrenylC-PseAAC: A Sequence-Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Prenylation Sites in Proteins. Journal of Theoretical Biology, 468, 1-11. https://doi.org/10.1016/j.jtbi.2019.02.007
Jun, Z. and Wang, S.Y. (2019) Identify Lysine Neddylation Sites Using Bi-Profile Bayes Feature Extraction via the Chou’s 5-Steps Rule and General Pseudo Components. Current Genomics, 20, 592-601. https://doi.org/10.2174/1389202921666191223154629
Lan, J., Liu, J., Liao, C., Merkler, D.J., Han, Q. and Li, J. (2019) A Study for Therapeutic Treatment against Parkinson’s Disease via Chou’s 5-Steps Rule. Current Topics in Medicinal Chemistry, 19, 2318-2333. http://www.eurekaselect.com/175887/article https://doi.org/10.2174/1568026619666191019111528
Liang, R., Xie, J., Zhang, C., Zhang, M., Huang, H., Huo, H., Cao, X. and Niu, B. (2019) Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-Steps Rule and General Pseudo Components. Current Topics in Medicinal Chemistry, 19, 2301-2317. https://doi.org/10.2174/1568026619666191016155543
Liang, Y. and Zhang, S. (2019) Identifying DNase I Hypersensitive Sites Using Multi-Features Fusion and F-Score Features Selection via Chou’s 5-Steps Rule. Biophysical Chemistry, 253, Article ID: 106227. https://doi.org/10.1016/j.bpc.2019.106227
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., Zhang, Q., Pan, Y., Su, D., Lu, Q. and 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
Akmal, M.A., Hussain, W., Rasool, N., Khan, Y.D., Khan, S.A. and Chou, K.C. (2020) Using Chou’s 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press. https://doi.org/10.1109/TCBB.2020.2968441
Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. International Journal of Molecular Sciences, 21, 75. https://doi.org/10.3390/ijms21010075
Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. Int. J. Mol. Sci. 2020, 21, 75. International Journal of Molecular Sciences, 21, 2629. https://doi.org/10.3390/ijms21072629
Chen, Y. and Fan, X. (2020) Use of Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 20, 220-230. https://doi.org/10.2174/1566524019666191011160543
Du, L., Meng, Q., Jiang, H. and Li, Y. (2020) Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule. IEEE Access, 8, 56452-56461. https://doi.org/10.1109/ACCESS.2020.2982160
Dutta, A., Dalmia, A., A. R, Singh, K.K. and Anand, A. (2020) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computers in Biology and Medicine, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
Ju, Z. and Wang, S.Y. (2020) Prediction of Lysine Formylation Sites Using the Composition of k-Spaced Amino Acid Pairs via Chou’s 5-Steps Rule and General Pseudo Components. Genomics, 112, 859-866. https://doi.org/10.1016/j.ygeno.2019.05.027
Kabir, M., Ahmad, S., Iqbal, M. and Hayat, M. (2020) iNR-2L: A Two-Level Sequence-Based Predictor Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying Nuclear Receptors and Their Families. Genomics, 112, 276-285. https://doi.org/10.1016/j.ygeno.2019.02.006
Lin, W., Xiao, X., Qiu, W. and Chou, K.C. (2020) Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis. Natural Science, 12, 181-198. https://doi.org/10.4236/ns.2020.123016
Vundavilli, H., Datta, A., Sima, C., Hua, J., Lopes, R. and Bittner, M. (2020) Using Chou’s 5-Steps Rule to Model Feedback in Lung Cancer. IEEE Journal of Biomedical and Health Informatics, in press. https://doi.org/10.1109/JBHI.2019.2958042
Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y. (2020) 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