Using subsite coupling to predict signal peptides

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

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A new method for predicting signal sequence cleavage sites

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Prediction of Protein Structural Classes

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Bioinformatics the machine learning approach

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Machine learning approaches for the prediction of signal peptides and other protein sorting signals

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Using neural networks for prediction of the subcellular location of proteins

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Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins

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Signal sequences

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Relation between amino acid composition and cellular location of proteins 1 1Edited by F. E. Cohen

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Protein subcellular location prediction

Chou, K., & Elrod, D. W. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.2.107 · Cited by 375

Extensive feature detection of N-terminal protein sorting signals

Bannai, H., Tamada, Y., Maruyama, O., Nakai, K., & Miyano, S. (2002) · Bioinform. · doi:10.1093/bioinformatics/18.2.298 · Cited by 746

iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Chen, W., Feng, P., Lin, H., & Chou, K. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1450 · Cited by 611

Prediction of linear B-cell epitopes using amino acid pair antigenicity scale

Chen, J., Liu, H., Yang, J., & Chou, K.-C. (2007) · Amino Acids · doi:10.1007/s00726-006-0485-9 · Cited by 552

Impacts of Bioinformatics to Medicinal Chemistry

Chou, K. (2015) · Medicinal Chemistry · doi:10.2174/1573406411666141229162834 · Cited by 518

Ipro54-pseknc: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

Lin, H., Deng, E.-Z., Ding, H., Chen, W., & Chou, K.-C. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1019 · Cited by 508

REVIEW : Recent advances in developing web-servers for predicting protein attributes

Chou, K., & Shen, H. (2009) · Natural Science · doi:10.4236/ns.2009.12011 · Cited by 463

iACP: a sequence-based tool for identifying anticancer peptides

Chen, W., Ding, H., Feng, P., Lin, H., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.7815 · Cited by 436

iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

Xu, Y., Ding, J., Wu, L., & Chou, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055844 · Cited by 389

Inuc-pseknc: A sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

Guo, S.-H., Deng, E.-Z., Xu, L.-Q., Ding, H., Lin, H., Chen, W., & Chou, K.-C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu083 · Cited by 375

Biochemical Genetics and Genomics of Insect Esterases

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pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.01.020 · Cited by 323

Performance measures in evaluating machine learning based bioinformatics predictors for classifications

Jiao, Y.-S., & Du, P.-F. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0081-2 · Cited by 308

iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition

Liu, B., Xu, J., Lan, X., Xu, R., Zhou, J., Wang, X., & Chou, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106691 · Cited by 306

iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC

Feng, P., Yang, H. Q., Ding, H., Lin, H., Chen, W., & Chou, K. (2018) · Genomics · doi:10.1016/j.ygeno.2018.01.005 · Cited by 308

A Brief History of Protein Sorting Prediction

Nielsen, H., Tsirigos, K. D., Brunak, S., & von HEIJNE, G. (2019) · The Protein Journal · doi:10.1007/s10930-019-09838-3 · Cited by 300

iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition

Feng, P., Chen, W., Lin, H., & Chou, K. (2013) · Analytical Biochemistry · doi:10.1016/j.ab.2013.05.024 · Cited by 294

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Feng, P., Ding, H., Yang, H. Q., Chen, W., Lin, H., & Chou, K. (2017) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2017.03.006 · Cited by 288

Signal-CF: A subsite-coupled and window-fusing approach for predicting signal peptides

Chou, K., & Shen, H. (2007) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2007.03.162 · Cited by 282

LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion

Chen, C., Zhang, Q., Ma, Q., & Yu, B. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.06.003 · Cited by 277

iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.04.011 · Cited by 275

iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteineS-nitrosylation sites in proteins

Xu, Y., Shao, X., Wu, L., Deng, N.-Y., & Chou, K. (2013) · PeerJ · doi:10.7717/peerj.171 · Cited by 272

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.12.009 · Cited by 272

pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties

Liu, Z., Xiao, X., Yu, D., Jia, J., Qiu, W., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.12.017 · Cited by 257

iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

Chen, W., Feng, P., Deng, E.-Z., Lin, H., & Chou, K. (2014) · Analytical Biochemistry · doi:10.1016/j.ab.2014.06.022 · Cited by 255

iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition

Liu, Z., Xiao, X., Qiu, W., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2014.12.009 · Cited by 252

Signal-3L: A 3-layer approach for predicting signal peptides

Shen, H., & Chou, K. (2007) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2007.08.140 · Cited by 241

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P. J., Ma, Q., & Zhang, Y. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103899 · Cited by 238

Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach

Liu, B., Fang, L., Liu, F., Wang, X., Chen, J., & Chou, K.-C. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121501 · Cited by 238

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Liu, B., Yang, F., & Chou, K. (2017) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2017.04.008 · Cited by 237

iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components

Qiu, W., Xiao, X., & Chou, K. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15021746 · Cited by 236

iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition

Xu, Y., Wen, X. L., Wen, L.-S., Wu, L., Deng, N.-Y., & Chou, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105018 · Cited by 233

iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels

Ding, H., Deng, E.-Z., Yuan, L., Liu, L., Lin, H., Chen, W., & Chou, K. (2014) · BioMed Research International · doi:10.1155/2014/286419 · Cited by 226

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

Song, J., Wang, Y., Li, F., Akutsu, T., Rawlings, N. D., Webb, G. I., & Chou, K. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby028 · Cited by 213

iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition

Chen, W., Feng, P., Lin, H., & Chou, K. (2014) · BioMed Research International · doi:10.1155/2014/623149 · Cited by 207

Prediction of signal peptides using scaled window

Chou, K. (2001) · Peptides · doi:10.1016/s0196-9781(01)00540-x · Cited by 207

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

Qiu, W., Jiang, S., Xu, Z., Xiao, X., & Chou, K. (2017) · Oncotarget · doi:10.18632/oncotarget.17104 · Cited by 207

iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

Chen, W., Lin, H., Feng, P., Ding, C., Zuo, Y., & Chou, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047843 · Cited by 201

iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition

Xu, Y., Wen, X. L., Shao, X., Deng, N.-Y., & Chou, K. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15057594 · Cited by 199

iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition

Chen, W., Ding, H., Zhou, X., Lin, H., & Chou, K. (2018) · Analytical Biochemistry · doi:10.1016/j.ab.2018.09.002 · Cited by 199

iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach

Qiu, W., Xiao, X., Lin, W., & Chou, K. (2014) · BioMed Research International · doi:10.1155/2014/947416 · Cited by 194

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC

Jia, J., Zhang, L., Liu, Z., Xiao, X., & Chou, K. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw387 · Cited by 192

Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique

Wei, L., Xing, P., Shi, G., Ji, Z., & Zou, Q. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2670558 · Cited by 187

Prediction of organellar targeting signals

Emanuelsson, O., & von HEIJNE, G. (2001) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/s0167-4889(01)00145-8 · Cited by 182

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space

Akbar, S., Hayat, M., Iqbal, M. J., & Jan, M. A. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.06.008 · Cited by 178

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.9148 · Cited by 176

iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model

Qiu, W., Xiao, X., Lin, W., & Chou, K. (2014) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2014.968875 · Cited by 174

iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier

Qiu, W., Sun, B., Xiao, X., Xu, Z., Jia, J., & Chou, K. (2017) · Genomics · doi:10.1016/j.ygeno.2017.10.008 · Cited by 174

Quokka : a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome

Li, F., Li, C., Marquez‐Lago, T. T., Leier, A., Akutsu, T., Purcell, A. W., Smith, A. I., Lithgow, T. J., Daly, R. J., Song, J., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty522 · Cited by 168

iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC

Qiu, W., Sun, B., Xiao, X., Xu, Z., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.10027 · Cited by 160

iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2016) · Molecules · doi:10.3390/molecules21010095 · Cited by 160

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

Le, N. Q. K., Yapp, E. K. Y., Ho, Q., Nagasundaram, N., Ou, Y., & Yeh, H. (2019) · Analytical Biochemistry · doi:10.1016/j.ab.2019.02.017 · Cited by 152

iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

Qiu, W., Xiao, X., Xu, Z., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.9987 · Cited by 150

iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

Liu, B., Fang, L., Liu, F., Wang, X., & Chou, K. (2015) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2015.1014422 · Cited by 149

Type I signal peptidases of Gram-positive bacteria

van Roosmalen, M. L., Geukens, N., Jongbloed, J. D. H., Tjalsma, H., Dubois, J. F., Bron, S., van Dijl, J. M., & Anné, J. (2004) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2004.05.006 · Cited by 146

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2015) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2015.1095116 · Cited by 145

iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences

Akbar, S., & Hayat, M. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.07.018 · Cited by 142

SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins

Hussain, W., Khan, Y. D., Rasool, N., Khan, S. A., & Chou, K. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.02.007 · Cited by 135

iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples

Kabir, M., & Hayat, M. (2015) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1108-5 · Cited by 133

iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC

Khan, Y. D., Rasool, N., Hussain, W., Khan, S. A., & Chou, K. (2018) · Analytical Biochemistry · doi:10.1016/j.ab.2018.04.021 · Cited by 129

Signal peptide prediction based on analysis of experimentally verified cleavage sites

Zhang, Z., & Henzel, W. J. (2004) · Protein Science · doi:10.1110/ps.04682504 · Cited by 123

SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins

Hussain, W., Khan, Y. D., Rasool, N., Khan, S. A., & Chou, K. (2018) · Analytical Biochemistry · doi:10.1016/j.ab.2018.12.019 · Cited by 122

PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only

Wei, L., Xing, P., Tang, J., & Zou, Q. (2017) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2017.2661756 · Cited by 121

Using deformation energy to analyze nucleosome positioning in genomes

Chen, W., Feng, P., Ding, H., Lin, H., & Chou, K. (2015) · Genomics · doi:10.1016/j.ygeno.2015.12.005 · Cited by 118

PseUI: Pseudouridine sites identification based on RNA sequence information

He, J., Fang, T., Zhang, Z., Huang, B., Zhu, X., & Xiong, Y. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2321-0 · Cited by 119

iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC

Jia, J., Li, X., Qiu, W., Xiao, X., & Chou, K. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.10.021 · Cited by 114

iCDI-PseFpt: Identify the channel–drug interaction in cellular networking with PseAAC and molecular fingerprints

Xiao, X., Min, J., Wang, P., & Chou, K. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.08.013 · Cited by 113

A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides

Xu, L., Liang, G., Wang, L., & Liao, C. (2018) · Genes · doi:10.3390/genes9030158 · Cited by 113

An overview on predicting the subcellular location of a protein.

Feng, Z. (2002) · PubMed · Cited by 112

High performance system for signal peptide prediction: SOSUIsignal

Gomi, M., Sonoyama, M., & Mitaku, S. (2004) · Chem-Bio Informatics Journal · doi:10.1273/cbij.4.142 · Cited by 108

An Efficient Classifier for Alzheimer’s Disease Genes Identification

Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107

Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5

Chou, K. (2004) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2004.02.098 · Cited by 105

Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

Ahmad, S., Kabir, M., & Hayat, M. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.07.005 · Cited by 101

iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC

Nazari, I., Tahir, M., Tayara, H., & Chong, K. T. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103811 · Cited by 100

Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs

Chou, K. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190507082559 · Cited by 97

iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition

Awais, M., Hussain, W., Khan, Y. D., Rasool, N., Khan, S. A., & Chou, K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919025 · Cited by 97

Predicting protein subcellular location by fusing multiple classifiers

Chou, K., & Shen, H. (2006) · Journal of Cellular Biochemistry · doi:10.1002/jcb.20879 · Cited by 97

Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition

Ahmad, K., Waris, M. A., & Hayat, M. (2016) · The Journal of Membrane Biology · doi:10.1007/s00232-015-9868-8 · Cited by 96

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Xu, L., Liang, G., Shi, S., & Liao, C. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061773 · Cited by 95

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

Xu, R., Zhou, J., Liu, B., He, Y., Zou, Q., Wang, X., & Chou, K. (2014) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2014.968624 · Cited by 95

iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule

Tahir, M., Tayara, H., & Chong, K. T. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.04.007 · Cited by 95

pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset

Chou, K., Cheng, X., & Xiao, X. (2018) · Genomics · doi:10.1016/j.ygeno.2018.08.007 · Cited by 94

A Defective Signal Peptide in a 19-kD α-Zein Protein Causes the Unfolded Protein Response and an Opaque Endosperm Phenotype in the Maize De*-B30 Mutant

Kim, C. S., Hunter, B. G., Kraft, J., Boston, R. S., Yans, S., Jung, R., & Larkins, B. A. (2004) · PLANT PHYSIOLOGY · doi:10.1104/pp.103.031310 · Cited by 94

Bidirectional Long Short-Term Memory Networks for Predicting the Subcellular Localization of Eukaryotic Proteins

Thireou, T. N., & Reczko, M. (2007) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.1015 · Cited by 95

iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking

Min, J., Xiao, X., & Chou, K. (2013) · BioMed Research International · doi:10.1155/2013/701317 · Cited by 90

A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule

Khan, S. A., Khan, M., Iqbal, N., Hussain, T., Khan, S. A., & Chou, K. (2019) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-019-09887-3 · Cited by 93

Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components

Ju, Z., & Wang, S. (2019) · Genomics · doi:10.1016/j.ygeno.2019.05.027 · Cited by 89

MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule

Du, X., Diao, Y., Liu, H., & Li, S. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00226 · Cited by 88

cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components

Akbar, S., Rahman, A. U., Hayat, M., & Sohail, M. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103912 · Cited by 88

Progresses in Predicting Post-translational Modification

Chou, K. (2019) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-019-09893-5 · Cited by 86

Prediction of the Tertiary Structure of the β-Secretase Zymogen

Chou, K., & Howe, W. J. (2002) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.2002.6686 · Cited by 84

A Novel Modeling in Mathematical Biology for Classification of Signal Peptides

Ehsan, A., Mahmood, K., Khan, Y. D., Khan, S. A., & Chou, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-19491-y · Cited by 84

iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule

Le, N. Q. K. (2019) · Molecular Genetics and Genomics · doi:10.1007/s00438-019-01570-y · Cited by 84

TargetM6A: Identifying N6-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine

Li, G.-Q., Liu, Z., Shen, H., & Yu, D. (2016) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2016.2599115 · Cited by 81

Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix

Waris, M. A., Ahmad, K., Kabir, M., & Hayat, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.025 · Cited by 80

pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments

Khan, Y. D., Jamil, M., Hussain, W., Rasool, N., Khan, S. A., & Chou, K. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.12.015 · Cited by 78

XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites

Liu, K., Chen, W., & Lin, H. (2019) · Molecular Genetics and Genomics · doi:10.1007/s00438-019-01600-9 · Cited by 76

iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

Fan, Y.-N., Xiao, X., Min, J., & Chou, K. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15034915 · Cited by 75

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

Xu, L., Liang, G., Liao, C., Chen, G., & Chang, C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00033 · Cited by 77

Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains

Krishnan, S. M. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.02.008 · Cited by 74

Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition

Qiu, W., Li, S., Cui, X., Yu, Z., Wang, M., Du, J., Peng, Y., & Yu, B. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.04.026 · Cited by 74

Prediction of signal peptides using scaled window

Chou, K. (2001) · Peptides · doi:10.1016/s0196-9781(01)00540-x · Cited by 207

Prediction of signal peptides using scaled window Kuo-Chen Chou*

(2001)

Using string kernel to predict signal peptide cleavage site based on subsite coupling model

Wang, M., Yang, J., & Chou, K.-C. (2005) · Amino Acids · doi:10.1007/s00726-005-0189-6 · Cited by 48

Signal-CF: A subsite-coupled and window-fusing approach for predicting signal peptides

Chou, K., & Shen, H. (2007) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2007.03.162 · Cited by 282

Prediction of Protein Signal Sequences

Chou, K. (2002) · Current Protein and Peptide Science · doi:10.2174/1389203023380468 · Cited by 85