Using subsite coupling to predict signal peptides

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

Nielsen, H., Engelbrecht, J., Brunak, S., & von HEIJNE, G. (1997) · Protein Engineering Design and Selection · doi:10.1093/protein/10.1.1 · Cited by 5,324 · see citing works

A new method for predicting signal sequence cleavage sites

von HEIJNE, G. (1986) · Nucleic Acids Research · doi:10.1093/nar/14.11.4683 · Cited by 4,554 · see citing works

Prediction of Protein Structural Classes

Chou, K., & Zhang, C. (1995) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409239509083488 · Cited by 1,058 · see citing works

Bioinformatics the machine learning approach

Baldi, P., & Brunak, S. (n.d.) · Cited by 925 · see citing works

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

Nielsen, H., Brunak, S., & von HEIJNE, G. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.1.3 · Cited by 609 · see citing works

Using neural networks for prediction of the subcellular location of proteins

Reinhardt, A. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.9.2230 · Cited by 597 · see citing works

Signal sequences

Gierasch, L. M. (1989) · Biochemistry · doi:10.1021/bi00429a001 · Cited by 424 · see citing works

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

Cedano, J. A., Aloy, P., Perez-Pons, J. A., & Querol, E. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0804 · Cited by 397 · see citing works

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 · see citing works

Transport of Proteins Across the Endoplasmic Reticulum Membrane

Rapoport, T. A. (1992) · Science · doi:10.1126/science.1332192 · Cited by 360 · see citing works

Protein sorting signals and prediction of subcellular localization

Nakai, K. (2000) · Advances in protein chemistry · doi:10.1016/s0065-3233(00)54009-1 · Cited by 320 · see citing works

Prediction of Protein Structural Classes and Subcellular Locations

Chou, K. (2000) · Current Protein and Peptide Science · doi:10.2174/1389203003381379 · Cited by 155 · see citing works

Prediction of N-terminal protein sorting signals

Claros, M. G., Brunak, S., & von HEIJNE, G. (1997) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(97)80057-7 · Cited by 122 · see citing works

Using Discriminant Function for Prediction of Subcellular Location of Prokaryotic Proteins

Chou, K., & Elrod, D. W. (1998) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.1998.9498 · Cited by 90 · see citing works

Protein Structure Prediction: A Practical Approach

Sternberg, M. J. E. (1997) · Oxford University Press eBooks · Cited by 85 · see citing works

Tertiary and quaternary conformational changes in aspartate transcarbamylase: a normal mode study

Thomas, A. M., Hinsen, K., Field, M. J., & Pérahia, D. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990101)34:1 · Cited by 64 · see citing works

The Practical Approach

Goossens, A. E., Karlberg, A., Basketter, D. A., & Lepoittevin, J. (1998) · Springer eBooks · doi:10.1007/978-3-642-80331-4_9 · Cited by 47 · see citing works

PHYSEAN: PHYsical SEquence ANalysis for the identification of protein domains on the basis of physical and chemical properties of amino acids

Ladunga, I. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.12.1028 · Cited by 27 · see citing works

Protein ZIP Codes Make Nobel Journey

Hagmann, M. (1999) · Science · doi:10.1126/science.286.5440.666 · Cited by 23 · see citing works

Prediction of secondary structure

King, R. D. (1996) · doi:10.1093/oso/9780199634972.003.0004 · Cited by 13 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Impacts of Bioinformatics to Medicinal Chemistry

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

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 · see citing works

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 · see citing works

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 434 · see citing works

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 · see citing works

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 · see citing works

Biochemical Genetics and Genomics of Insect Esterases

Oakeshott, J. G., Claudianos, C., Campbell, P. M., Newcomb, R. D., & Russell, R. J. M. (2005) · Elsevier eBooks · doi:10.1016/b0-44-451924-6/00073-9 · Cited by 331 · see citing works

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 322 · see citing works

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 · see citing works

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 · see citing works

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 306 · see citing works

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 301 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 270 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 238 · see citing works

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 · see citing works

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 232 · see citing works

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 225 · see citing works

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 211 · see citing works

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 · see citing works

Prediction of signal peptides using scaled window

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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 177 · see citing works

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 · see citing works

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 · see citing works

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 172 · see citing works

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 167 · see citing works

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 · see citing works

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 159 · see citing works

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 · see citing works

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 · see citing works

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 148 · see citing works

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 · see citing works

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 · see citing works

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 143 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 112 · see citing works

An overview on predicting the subcellular location of a protein.

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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 90 · see citing works

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 · see citing works

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 90 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 82 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Prediction of signal peptides using scaled window

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

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 · see citing works

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 · see citing works

Prediction of Protein Signal Sequences

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