Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences — Oak Academic Publishing
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Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences
Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
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School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
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Gordon Life Science Institute, Bostom, Massachusetts, USA
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Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, KSA
1 Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
2 School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
3 Gordon Life Science Institute, Bostom, Massachusetts, USA
4 Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
5 Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, KSA
Pse-in-One 2.0 is a package of web-servers evolved from Pse-in-One (Liu, B., Liu, F., Wang, X., Chen, J. Fang, L. & Chou, K.C. Nucleic Acids Research, 2015, 43:W65-W71). In order to make it more flexible and comprehensive as suggested by many users, the updated package has incorporated 23 new pseudo component modes as well as a series of new feature analysis approaches. It is available at http://bioinformatics.hitsz.edu.cn/Pse-in-One2.0/ . Moreover, to maximize the convenience of users, provided is also the stand-alone version called “Pse-in-One-Analysis”, by which users can significantly speed up the analysis of massive sequences.
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Kandaswamy, K.K., Pugalenthi, G., Moller, S., Hartmann, E., Kalies, K.U., Suganthan, P.N. and Martinetz, T. (2010) Prediction of Apoptosis Protein Locations with Genetic Algorithms and Sup-port Vector Machines Through a New Mode of Pseudo Amino Acid Composition. Protein and Peptide Letters, 17, 1473-1479.
Liu, T., Zheng, X., Wang, C. and Wang, J. (2010) Prediction of Subcellular Location of Apoptosis Proteins Using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation. Protein & Peptide Letters, 17, 1263-1269. https://doi.org/10.2174/092986610792231528
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
Niu, X.H., Li, N.N., Shi, F., Hu, X.H., Xia, J.B. and Xiong, H.J. (2010) Predicting Protein Solubility with a Hybrid Approach by Pseudo Amino Acid Composition. Protein and Peptide Letters, 17, 1466-1472. https://doi.org/10.2174/0929866511009011466
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.
Wang, Y.C., Wang, X.B., Yang, Z.X. and Deng, N.Y. (2010) Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature. Protein & Peptide Letters, 17, 1441-1449. https://doi.org/10.2174/0929866511009011441
Wu, J., Li, M.L., Yu, L.Z. and Wang, C. (2010) An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo Amino Acid Composition. The Protein Journal, 29, 62-67. https://doi.org/10.1007/s10930-009-9222-z
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
Chou, K.C. and Shen, H.B. (2010) Cell-PLoc 2.0: An Improved Package of Web-Servers for Predicting Subcellular Localization of Proteins in Various Organisms. Natural Science, 2, 1090-1103.
Chou, K.C. (2011) Some Remarks on Protein Attribute Prediction and Pseudo Amino Acid Composition (50th Anniversary Year Review). Journal of Theoretical Biology, 273, 236-247. https://doi.org/10.1016/j.jtbi.2010.12.024
Ding, H., Liu, L., Guo, F.B., Huang, J. and Lin, H. (2011) Identify Golgi Protein Types with Modified Mahalanobis Discriminant Algorithm and Pseudo Amino Acid Composition. Protein & Peptide Letters, 18, 58-63.
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
Hayat, M. and Khan, A. (2011) Predicting Membrane Protein Types by Fusing Composite Protein Sequence Features into Pseudo Amino Acid Composition. Journal of Theoretical Biology, 271, 10-17. https://doi.org/10.1016/j.jtbi.2010.11.017
Hu, L., Zheng, L., Wang, Z., Li, B. and Liu, L. (2011) Using Pseudo Amino Acid Composition to Predict Protease Families by Incorporating a Series of Protein Biological Features. Protein and Peptide Letters, 18, 552-558. https://doi.org/10.2174/092986611795222795
Huang, Y., Yang, L. and Wang, T. (2011) Phylogenetic Analysis of DNA Sequences Based on the Generalized Pseudo Amino Acid Composition. Journal of Theoretical Biology, 269, 217-223. https://doi.org/10.1016/j.jtbi.2010.10.027
Jingbo, X., Silan, Z., Feng, S., Huijuan, X., Xuehai, H., Xiaohui, N. and Zhi, L. (2011) Using the Concept of Pseudo Amino Acid Composition to predict Resistance Gene against Xanthomonas oryzae pv. oryzae in Rice: An Approach from Chaos Games Representation. Journal of Theoretical Biology, 284, 16-23. https://doi.org/10.1016/j.jtbi.2011.06.003
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, 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.
Liu, X.L., Lu, J.L. and Hu, X.H. (2011) Predicting Thermophilic Proteins with Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Principal Component Analysis. Protein & Peptide Letters, 18, 1244-1250.
Mahdavi, A. and Jahandideh, S. (2011) Application of Density Similarities to Predict Membrane Protein Types Based on Pseudo Amino Acid Composition. Journal of Theoretical Biology, 276, 132-137. https://doi.org/10.1016/j.jtbi.2011.01.048
Mohabatkar, H., Mohammad Beigi, M. and Esmaeili, A. (2011) Prediction of GABA(A) 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 Beigi, 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., Sun, X.Y., Suo, S.B., Shi, S.P., Huang, S.Y., Liang, R.P. and Zhang, L. (2011) Predicting Homo-Oligomers and Hetero-Oligomers by Pseudo Amino Acid Composition: An Approach from Discrete Wavelet Transformation. Biochimie, 93, 1132-1138. https://doi.org/10.1016/j.biochi.2011.03.010
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
Shi, R. and Xu, C. (2011) Prediction of Rat Protein Subcellular Localization with Pseudo Amino Acid Composition Based on Multiple Sequential Features. Protein and Peptide Letters, 18, 625-633. https://doi.org/10.2174/092986611795222768
Shu, M., Cheng, X., Zhang, Y., Wang, Y., Lin, Y., Wang, L. and Lin, Z. (2011) Predicting the Activity of ACE Inhibitory Peptides with a Novel Mode of Pseudo Amino Acid Composition. Protein & Peptide Letters, 18, 1233-1243.
Wang, D., Yang, L., Fu, Z. and Xia, J. (2011) Prediction of Thermophilic Protein with Pseudo Amino Acid Composition: An Approach from Combined Feature Selection and Reduction. Protein & Peptide Letters, 18, 684-689. https://doi.org/10.2174/092986611795446085
Wang, W., Geng, X.B., Dou, Y., Liu, T. and Zheng, X. (2011) Predicting Protein Subcellular Localization by Pseudo Amino Acid Composition with a Segment-Weighted and Features-Combined Approach. Protein and Peptide Letters, 18, 480-487. https://doi.org/10.2174/092986611794927947
Xiao, X. and Chou, K.C. (2011) Using Pseudo Amino Acid Composition to Predict Protein Attributes via Cellular Automata and Other Approaches .Current Bioinformatics, 6, 251-260. https://doi.org/10.2174/1574893611106020251
Xiao, X., Wang, P. and Chou, K.C. (2011) GPCR-2L: Predicting G Protein-Coupled Receptors and Their Types by Hybridizing Two Different Modes of Pseudo Amino Acid Compositions. Molecular Biosystems, 7, 911-919. https://doi.org/10.1039/C0MB00170H
Zia Ur, R. and Khan, A. (2011) Prediction of GPCRs with Pseudo Amino Acid Composition: Employing Composite Features and Grey Incidence Degree Based Classification. Protein & Peptide Letters, 18, 872-878.
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.
Chen, Y.L., Li, Q.Z. and Zhang, L.Q. (2012) Using Increment of Diversity to Predict Mitochondrial Proteins of Malaria Parasite: Integrating Pseudo Amino Acid Composition and Structural Alphabet. Amino Acids, 42, 1309-1316. https://doi.org/10.1007/s00726-010-0825-7
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
Gao, Q.B., Zhao, H., Ye, X. and He, J. (2012) Prediction of Pattern Recognition Receptor Family Using Pseudo Amino Acid Composition. Biochemical and Biophysical Research Communications, 417, 73-77. https://doi.org/10.1016/j.bbrc.2011.11.057
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.
Lin, W.Z., Fang, J.A., Xiao, X. and Chou, K.C. (2012) Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model. PLoS ONE, 7, e49040. https://doi.org/10.1371/journal.pone.0049040
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 Biolology and Bioinformatics, 9, 467-475.
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.
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.
Wang, J., Li, Y., Wang, Q., You, X., Man, J., Wang, C. and Gao, X. (2012) ProClusEnsem: Predicting Membrane Protein Types by Fusing Different Modes of Pseudo Amino Acid Composition. Computers in Biology and Medicine, 42, 564-574. https://doi.org/10.1016/j.compbiomed.2012.01.012
Yu, X., Zheng, X., Liu, T., Dou, Y. and Wang, J. (2012) Predicting Subcellular Location of Apoptosis Proteins with Pseudo Amino Acid Composition: Approach from Amino Acid Substitution Matrix and Auto Covariance Transformation. Amino Acids, 42, 1619-1625. https://doi.org/10.1007/s00726-011-0848-8
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, K.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.
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. 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.
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 Biomethmatics, 6, 1350003.
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 Number: 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.
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.
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
Xiaohui, N., Nana, L., Jingbo, X., Dingyan, C., Yuehua, P., Yang, X., Weiquan, W., Dongming, W. and Zengzhen, W. (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
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
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.
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
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
Mondal, S. and Pai, P.P. (2014) Chou’s Pseudo Amino Acid Composition Improves Sequence-Based Antifreeze Protein Prediction. Journal of Theoretical Biology, 356, 30-35. https://doi.org/10.1016/j.jtbi.2014.04.006
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
Qiu, W.R., Xiao, X. and Chou, K.C. (2014) iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components. International Journal of Molecular Sciences (IJMS), 15, 1746-1766.
Qiu, W.R., Xiao, X., Lin, W.Z. and Chou, K.C. (2014) iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach. BioMed Research International (BMRI), 2014, 947416.
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 (IJMS), 15, 7594-7610.
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
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
Ding, H., Deng, E.Z., Yuan, L.F., Liu, L., Lin, H., Chen, W. and Chou, K.C. (2014) iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels. BioMed Research International (BMRI), 2014, 286419.
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.
Chen, L., Chu, C., Huang, T., Kong, X. and Cai, Y.D. (2015) Prediction and Analysis of Cell-Penetrating Peptides Using Pseudo Amino Acid Composition and Random Forest Models. Amino Acids.
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.
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
Liu, B., Chen, J. and Wang, X. (2015) Protein Remote Homology Detection by Combining Chou's Distance-Pair Pseudo Amino Acid Composition and Principal Component Analysis. Molecular Genetics and Genomics, 290, 1919-1931. https://doi.org/10.1007/s00438-015-1044-4
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
Xu, R., Zhou, J., Liu, B., He, Y.A., Zou, Q., Wang, X. and Chou, K.C. (2015) Identification of DNA-Binding Proteins by Incorporating Evolutionary Information into Pseudo Amino Acid Composition via the Top-n-Gram Approach. Journal of Biomolecular Structure & Dynamics (JBSD), 33, 1720-1730.
Zhu, P.P., Li, W.C., Zhong, Z.J., Deng, E.Z., Ding, H., Chen, W. and Lin, H. (2015) Predicting the Subcellular Localization of Mycobacterial Proteins by Incorporating the Optimal Tripeptides into the General Form of Pseudo Amino Acid Composition. Molecular BioSystems, 11, 558-563. https://doi.org/10.1039/C4MB00645C
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
Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K. and Sattar, A. (2015) Gram-Positive and Gram-Negative Protein Sub-cellular 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
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
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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
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. Pocess Biochemistry, 48, 1048-1053.
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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 Pse-AAC. Protein Engineering, Design and Selection, 26, 735-742. https://doi.org/10.1093/protein/gzt042
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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., 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., Feng, P.M., Lin, H. and Chou, K.C. (2014) iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition. BioMed Research International (BMRI), 2014, 623149.
Guo, S.H., Deng, E.Z., Xu, L.Q., Ding, H., Lin, H., Chen, W. and Chou, K.C. (2014) iNuc-PseKNC: A Sequence-Based Predictor for Predicting Nucleosome Positioning in Genomes with Pseudo k-Tuple Nucleotide Composition. Bioinformatics, 30, 1522-1529. https://doi.org/10.1093/bioinformatics/btu083
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
Xiao, X., Ye, H.X., Liu, Z., Jia, J.H. and Chou, K.C. (2016) iROS-gPseKNC: Predicting Replication Origin Sites in DNA by Incorporating Dinucleotide Position-Specific Propensity into General Pseudo Nucleotide Composition. Oncotarget, 7, 34180-34189.
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
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. https://doi.org/10.1016/j.omtn.2017.03.006
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., 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., 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
Ahmad, K., Waris, M. and Hayat, M. (2016) Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition. Journal of Membrane Biology, 249, 293-304.
Behbahani, M., Mohabatkar, H. and Nosrati, M. (2016) Analysis and Comparison of Lignin Peroxidases between Fungi and Bacteria Using Three Different Modes of Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 411, 1-5. https://doi.org/10.1016/j.jtbi.2016.09.001
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.
Fan, G.L., Liu, Y.L. and Wang, H. (2016) Identification of Thermophilic Proteins by Incorporating Evolutionary and Acid Dissociation Information into Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 407, 138-142. https://doi.org/10.1016/j.jtbi.2016.07.010
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 & Dynamics (JBSD), 34, 1946-1961. https://doi.org/10.1080/07391102.2015.1095116
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.
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
Liu, B., Long, R. and Chou, K.C. (2016) iDHS-EL: Identifying DNase I Hypersensitive Sites 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
Liu, Z., Xiao, X., Yu, D.J., Jia, J., Qiu, W.R. and Chou, K.C. (2016) pRNAm-PC: Predicting N-Methyladenosine Sites in RNA Sequences via Physical-Chemical Properties. Analytical Biochemistry, 497, 60-67. https://doi.org/10.1016/j.ab.2015.12.017
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.
Qiu, W.R., Xiao, X., Xu, Z.H. and Chou, K.C. (2016) iPhos-PseEn: Identifying Phosphorylation Sites in Proteins by Fusing Different Pseudo Components into an Ensemble Classifier. Oncotarget, 7, 51270-51283.
Qiu, W.R., Zheng, Q.S., Sun, B.Q. and Xiao, X. (2016) Multi-iPPseEvo: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory Mol Inform. https://doi.org/10.1002/minf.201600010
Rahimi, M., Bakhtiarizadeh, M.R. and Mohammadi-Sangcheshmeh, A. (2016) 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
Sun, W.J., Li, J.H., Liu, S., Wu, J., Zhou, H., Qu, L.H. and Yang, J.H. (2016) RMBase: A Resource for Decoding the Landscape of RNA Modifications from High-Throughput Sequencing Data. Nucleic Acids Research, 44, D259-D265. https://doi.org/10.1093/nar/gkv1036
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
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
Xu, C., Sun, D., Liu, S. and Zhang, Y. (2016) Protein Sequence Analysis by Incorporating Modified Chaos Game and Physico-chemical Properties into Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 406, 105-115.
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
Zhang, C.J., Tang, H., Li, W.C., Lin, H., Chen, W. and Chou, K.C. (2016) iOri-Human: Identify Human Origin of Replication by Incorporating Dinucleotide Physicochemical Properties into Pseudo Nucleotide Composition. Oncotarget, 7, 69783-69793.
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. Journal of Membrane Biology, 249, 23-29.
Zou, H.L. and Xiao, X. (2016) Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition. Journal of Membrane Biology, 249, 561-567.
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
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
Kabir, M. and Hayat, M. (2016) iR-Spot-GAEnsC: Identifying Recombination Spots via Ensemble Classifier and Extending the Concept of Chou’s Pse-AAC 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 Pse-AAC. Mol Biosyst, 12, 2587-2593. https://doi.org/10.1039/C6MB00221H
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
Jiao, Y.S. and Du, P.F. (2017) Predicting Protein Submitochondrial Locations by Incorporating the Positional-Specific Physicochemical Properties into Chou’s General Pseudo-Amino Acid Compositions. Journal of Theoretical Biology, 416, 81-87. https://doi.org/10.1016/j.jtbi.2016.12.026
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, 4208-4217. https://doi.org/10.18632/oncotarget.14524
Qiu, W.R., Jiang, S.Y., Xu, Z.C., Xiao, X. and Chou, K.C. (2017) iRNAm5C-PseDNC: Identifying RNA 5-Me-thylcytosine Sites by Incorporating Physical-Chemical Properties into Pseudo Dinucleotide Composition. Oncotarget, in press (026264R2).
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. Mol Inform, 36. https://doi.org/10.1002/minf.201600085
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
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
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, in press.
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, 42362. https://doi.org/10.1038/srep42362
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, in press.
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Qiu, W.R., Jiang, S.Y., Sun, B.Q., Xiao, X. and Chou, K.C. (2017) iRNA-2methyl: An Ensemble Classifier for Identifying RNA 2'-O-Methylation Modification Sites by Incorporating Sequence-Coupled Effects into General PseKNC. Genomics, in press.
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He, Z., Zhang, J., Shi, X.H., Hu, L.L., Kong, X., Cai, Y.D. and Chou, K.C. (2010) Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features. PLoS ONE, 5, e9603. https://doi.org/10.1371/journal.pone.0009603
Hu, L., Huang, T., Shi, X., Lu, W.C., Cai, Y.D. and Chou, K.C. (2011) Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties. PLoS ONE, 6, e14556. https://doi.org/10.1371/journal.pone.0014556
Huang, R.B., Du, Q.S., Wang, C.H., Liao, S.M. and Chou, K.C. (2010) A Fast and Accurate Method for Predicting pKa of Residues in Proteins. Protein Engineering, Design and Selection (PEDS), 23, 35-42.
Huang, T., Chen, L., Cai, Y.D. and Chou, K.C. (2011) Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property. PLoS ONE, 6, e25297. https://doi.org/10.1371/journal.pone.0025297
Huang, T., Niu, S., Xu, Z., Huang, Y., Kong, X., Cai, Y.D. and Chou, K.C. (2011) Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties. PLoS ONE, 6, e22940.
Huang, T., Shi, X.H., Wang, P., He, Z., Feng, K.Y., Hu, L., Kong, X., Li, Y.X., Cai, Y.D. and Chou, K.C. (2010) Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks. PLoS ONE, 5, e10972. https://doi.org/10.1371/journal.pone.0010972
Huang, T., Zhang, J., Xu, Z.P., Hu, L.L., Chen, L., Shao, J.L., Zhang, L., Kong, X.Y., Cai, Y.D. and Chou, K.C. (2012) Deciphering the Effects of Gene Deletion on Yeast Longevity Using Network and Machine Learning Approaches. Biochimie, 94, 1017-1025. https://doi.org/10.1016/j.biochi.2011.12.024
Li, B.Q., Hu, L.L., Chen, L., Feng, K.Y., Cai, Y.D. and Chou, K.C. (2012) Prediction of Protein Domain with mRMR Feature Selection and Analysis. PLoS ONE, 7, e39308.
Li, B.Q., Hu, L.L., Niu, S., Cai, Y.D. and Chou, K.C. (2012) Predict and Analyze S-Nitrosylation Modification Sites with the mRMR and IFS Approaches. Journal of Proteomics, 75, 1654-1665. https://doi.org/10.1016/j.jprot.2011.12.003
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