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

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

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

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

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

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

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

Prediction of Signal Peptides Using Bio-Basis Function Neural Networks and Decision Trees

Sidhu, A., & Yang, Z. R. (2006) · Applied Bioinformatics · doi:10.2165/00822942-200605010-00002 · Cited by 12

A comprehensive tool for accurate identification of methyl-Glutamine sites

Malebary, S. J., Alzahrani, E. O., & Khan, Y. D. (2021) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2021.108074 · Cited by 11

pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

Liu, X., & Chou, K. (2020) · Advances in Bioscience and Biotechnology · doi:10.4236/abb.2020.115011 · Cited by 10

pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning

Shao, Y., & Chou, K. (2020) · Natural Science · doi:10.4236/ns.2020.126034 · Cited by 10

Structural and functional organization of the signal peptide of pro-enterotoxin B from Staphylococcus aureus

Mordkovich, N. N., Okorokova, N. A., & Вейко, В. П. (2015) · Applied Biochemistry and Microbiology · doi:10.1134/s0003683815060101 · Cited by 10

iPseU-Layer: Identifying RNA Pseudouridine Sites Using Layered Ensemble Model

Mu, Y., Zhang, R., Wang, L., & Liu, X. (2020) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00362-y · Cited by 10

βLact‐Pred: A Predictor Developed for Identification of Beta‐Lactamases Using Statistical Moments and PseAAC via 5‐Step Rule

Ashraf, M. A., Khan, Y. D., Shoaib, B., Khan, M. A., Khan, F. N., & Whangbo, T. K. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8974265 · Cited by 9

iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations

Naseer, S., Fati, S. M., Muneer, A., & Ali, R. F. (2022) · IEEE Access · doi:10.1109/access.2022.3144226 · Cited by 9

Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition

Liu, Z., Xiao, X., Qiu, W., & Chou, K. (2015) · Data in Brief · doi:10.1016/j.dib.2015.04.021 · Cited by 9

An Insightful Recollection for Predicting Protein Subcellular Locations in Multi-Label Systems

Chou, K. (2020) · Natural Science · doi:10.4236/ns.2020.127036 · Cited by 8

Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach

Krishnan, S. M. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.08.027 · Cited by 8

RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression

Tarafder, S., Ahmed, M. T., Iqbal, S., Hoque, M. T., & Rahman, M. S. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.12.029 · Cited by 8

Protein–protein interaction site predictions with minimum covariance determinant and Mahalanobis distance

Qiu, Z., Zhou, B., & Yuan, J. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.08.026 · Cited by 7

PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence

Khandelwal, M., & Rout, R. K. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05491-x · Cited by 7

Prediction of Signal Peptide Cleavage Sites with Subsite‐Coupled and Template Matching Fusion Algorithm

Zhang, S., Zhang, T., Zhang, J., & Huang, Y. (2014) · Molecular Informatics · doi:10.1002/minf.201300077 · Cited by 7

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites

Khandelwal, M., & Rout, R. K. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae001 · Cited by 6

ILYCROsite: Identification of lysine crotonylation sites based on FCM-GRNN undersampling technique

Zuo, Y., Wan, M., Shen, Y., Wang, X., He, W., Bi, Y., Liu, X., & Deng, Z. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108212 · Cited by 6

Signal peptide discrimination and cleavage site identification using SVM and NN

Kazemian, H. B., Yusuf, S. A., & White, K. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.11.017 · Cited by 6

Influence of signal‐peptide truncations on the functional expression of Escherichia coli γ ‐glutamyltranspeptidase

Lo, H., Chou, W., Chen, P., & Lin, L. (2008) · Journal of Basic Microbiology · doi:10.1002/jobm.200700325 · Cited by 6

iHyd-ProSite: A novel Computational Approach for Identifying Hydroxylation Sites in Proline Via Mathematical ModelingPreprint

Mahmood, M. K., Ehsan, A., & Khan, Y. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.03.974717 · Cited by 6

Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction

Alyahya, A. N., Khan, S., Dilshad, N., & Ahmad, N. (2026) · Scientific Reports · doi:10.1038/s41598-026-41489-0 · Cited by 5

A Novel Sequence-Based Method for Phosphorylation Site Prediction with Feature Selection and Analysis

He, Z., Shi, X., Kong, X., Zhu, Y., & Chou, K. (2012) · Protein and Peptide Letters · doi:10.2174/092986612798472893 · Cited by 5

Protein–protein interaction site prediction using random forest proximity distance

Qiu, Z., & Liu, Q.-J. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720020500420 · Cited by 5

IMPROVED SEQUENCE-BASED PREDICTION OF STRAND RESIDUES

Kedarisetti, K. D., Mizianty, M. J., Dick, S., & Kurgan, L. (2010) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720011005355 · Cited by 5

Predicting Protein N-Terminal Signal Peptides Using Position-Specific Amino Acid Propensities and Conditional Random Fields

Fan, Y., Song, J., Chen, X., & Shen, H. (2013) · Current Bioinformatics · doi:10.2174/1574893611308020006 · Cited by 4

Evolutionary insights into the active-site structures of the metallo-β-lactamase superfamily from a classification study with support vector machine

Wang, L., Yang, L., Feng, Y., & Zhang, H. (2020) · JBIC Journal of Biological Inorganic Chemistry · doi:10.1007/s00775-020-01822-y · Cited by 4

pLoc_Deep-mGpos: Predict Subcellular Localization of Gram Positive Bacteria Proteins by Deep Learning

Lu, Z., & Chou, K. (2020) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2020.135005 · Cited by 4

Effects of neighboring sequence environment in predicting cleavage sites of signal peptides

Li, Y., Wen, Z., Zhou, C., Tan, F., & Li, M. (2008) · Peptides · doi:10.1016/j.peptides.2008.04.011 · Cited by 4

Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

Zheng, Z., Chen, Y., Chen, L., Guo, G., Fan, Y., & Kong, X. (2012) · BioMed Research International · doi:10.1155/2012/492174 · Cited by 4

Prediction of Membrane Proteins in Mycobacterium tuberculosis Using a Support Vector Machine Algorithm

Yeh, J. I., & Mao, L. (2006) · Journal of Computational Biology · doi:10.1089/cmb.2006.13.126 · Cited by 3

Predicting Signal Peptides and Their Cleavage Sites Using Support Vector Machines and Improved Position Weight Matrixes

Sun, J., & Wang, L. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.406 · Cited by 3

The evolutionary relationship of S15/NS1RNA binding domains with a similar protein domain pattern - A computational approach

Krishnan, S. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100611 · Cited by 2

A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins

Gu, X., Ding, Y., Xiao, P., & He, T. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.935717 · Cited by 2

Robust Deep Neural Network-Based Framework for Predicting and Classifying Capsid Protein Based on Biomedical Data

Khattak, A. U. R., Ullah, A., Rehman, A. U., Mahmood, T., Khattak, Q. W., Alotaibi, S., & Bahaj, S. A. (2023) · IEEE Access · doi:10.1109/access.2023.3319485 · Cited by 2

Prediction of success for polymerase chain reactions using the Markov maximal order model and support vector machine

Li, C., Yang, Y., Fei, W., He, P., Yu, X., Zhang, D., Yi, S., Li, X., Zhu, J., Wang, C., et al. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.01.017 · Cited by 2

Computer-Assisted analysis of subcellular localization signals and post-translational modifications of human prion proteins

Moosawi, F., & Mohabatkar, H. (2009) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2009.21012 · Cited by 2

Identification of Phage Virion Proteins by Using the g-gap Tripeptide Composition

Yang, L., Gao, H., Liu, Z., & Tang, L. (2018) · Letters in Organic Chemistry · doi:10.2174/1570178615666180910112813 · Cited by 2

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

Amin, A., Awais, M., Sahai, S., Hussain, W., & Rasool, N. (2021) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-021-10170-7 · Cited by 2

Metabolic Pathway Modeling by Using the Nearest Neighbor AlgorithmPreprint

Cai, Y., & Muldoon, M. R. (2007) · MIMS EPrints (University of Southampton) · Cited by 2

Identification of secretory proteins based on similarity of amino acid sequences

Liu, H., Liu, X., & Yao, Y. (2010) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2010.5639769 · Cited by 1

A Novel Approach Predicting the Signal Peptides and Their Cleavage Sites

Yao, L., Li, X., Liu, H., & Chou, K. (2007) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2007.103 · Cited by 1

A computational model for predicting integrase catalytic domain of retrovirus

Wu, S., Han, J., Zhang, X., Zhong, D., & Liu, R. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.04.020 · Cited by 1

A Technique for Prediction Cytokines based On Statistical Moments and a Random Forest Classifier

Ashraf, A., & Ashraf, R. A. R. (2021) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v9i1.744 · Cited by 1

SulfoTyr-PseAAC: A Machine Learning Framework to Identify Sulfotyrosine Sites

Shah, A. A., & Khan, Y. D. (2022) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct55600.2022.10146792 · Cited by 1

Identification of Secretory Proteins by Separated Space Based Linear Discriminant Analysis

Liu, H., Yao, Y., Liu, X., & Chou, K. (2008) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2008.824 · Cited by 1

Preparing a human membrane and secreted protein-enriched cDNA library using PCR primers derived from a genomic database

Fan, Y. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.22.e114 · Cited by 1

Prediction of signal peptide cleavage sites with template matching fusion algorithm

Du, X., & Zhang, S. (2010) · International Conference on Signal Processing · doi:10.1109/icosp.2010.5656676

Using temperature effects to predict the interactions between two RNAs

Ganjtabesh, ‎., Montaseri, S., & Zare‐Mirakabad, F. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.09.001

Predicting signal peptide and its cleavage site by using GA-optimized position weight matrices

Lao, D. M., & Avila, J. M. C. (2012) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2012.6412173

Improve signal peptide prediction by using functional domain information

Zhang, Y.-Z., & Shen, H. (2016) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2016.7853012

Computational Prediction of Strand Residues from Protein Sequences

Kedarisetti, K. D. (2012) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r30s3x

m6ABRP: Predicting m6A-YTHDF2 Binding Regions via Sequence-based Properties

Liu, Z., Xu, J., Mao, X., Qi, J., & Li, Q. (2020) · International Conference on Bioinformatics and Biomedical Science (ICBBS) · doi:10.1145/3431943.3431950

Identification of intelligence-related proteins through a robust two-layer predictor

Shomali, A., Sadi, M. S. V., Bakhtiarizadeh, M. R., Aliniaeifard, S., TREWAVAS, A. J., & Calvo, P. (2022) · Communicative & Integrative Biology · doi:10.1080/19420889.2022.2143101

An Explainable Identifier of iGHBPs Peptides Based on Deep PSSM Features and Learning Approaches

Sikander, R., Rehman, M., Brohi, T. A., Ahmed, A., Ghulam, A., & Ahmed, S. (2024) · Insights-Journal of Health and Rehabilitation · doi:10.71000/7dqqxs92

Hybrid-DeepLSTM: statistical analysis-based classification of long non-coding RNAs (lncRNAs) in plant genomes using a computational hybrid model enhanced with LSTM layers

Aljeddani, S. M. A. (2025) · Journal of Umm Al-Qura University for Engineering and Architecture · doi:10.1007/s43995-025-00128-x

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