Amino acid encoding methods for protein sequences: a comprehensive review and assessment

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works

Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features

Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works

Principles that Govern the Folding of Protein Chains

Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096 · see citing works

SCOP: A structural classification of proteins database for the investigation of sequences and structures

Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,412 · see citing works

Amino acid substitution matrices from protein blocks.

Henikoff, S., & Henikoff, J. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.22.10915 · Cited by 6,218 · see citing works

UniProt: a hub for protein information

The UniProt Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku989 · Cited by 5,106 · see citing works

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288 · see citing works

PISCES: a protein sequence culling server

Wang, G., & Dunbrack, R. L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg224 · Cited by 1,664 · see citing works

Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation

Miyazawa, S., & Jernigan, R. L. (1985) · Macromolecules · doi:10.1021/ma00145a039 · Cited by 1,588 · see citing works

Deep learning in bioinformatics

Min, S., Lee, B., & Yoon, S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw068 · Cited by 1,514 · see citing works

Residue – Residue Potentials with a Favorable Contact Pair Term and an Unfavorable High Packing Density Term, for Simulation and Threading

Miyazawa, S., & Jernigan, R. L. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0114 · Cited by 1,231 · see citing works

Hydrophobicity of Amino Acid Residues in Globular Proteins

Rose, G. D., Geselowitz, A., Lesser, G. J., Lee, R., & Zehfus, M. H. (1985) · Science · doi:10.1126/science.4023714 · Cited by 1,176 · see citing works

AAindex: amino acid index database, progress report 2008

Kawashima, S., Pokarowski, P., Pokarowska, M., Koliński, A., Katayama, T., & Kanehisa, M. I. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm998 · Cited by 1,131 · see citing works

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

Asgari, E., & Mofrad, M. R. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141287 · Cited by 817 · see citing works

SCOPe: Structural Classification of Proteins--extended, integrating SCOP and ASTRAL data and classification of new structures

Fox, N. K., Brenner, S. E., & Chandonia, J.-M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1240 · Cited by 762 · see citing works

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., & Chou, K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv458 · Cited by 723 · see citing works

Iterated profile searches with PSI-BLAST—a tool for discovery in protein databases

Altschul, S. F., & Koonin, E. V. (1998) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(98)01298-5 · Cited by 679 · see citing works

Critical assessment of methods of protein structure prediction (CASP) — round x

Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24452 · Cited by 673 · see citing works

Evaluation and improvement of multiple sequence methods for protein secondary structure prediction

Cuff, J. A., & Barton, G. J. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990301)34:4 · Cited by 660 · see citing works

Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites in E. coli

Stormo, G. D., Schneider, T. D., Gold, L., & Ehrenfeucht, A. (1982) · Nucleic Acids Research · doi:10.1093/nar/10.9.2997 · Cited by 651 · see citing works

Comparing the polarities of the amino acids: side-chain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution

Radzicka, A., & Wolfenden, R. V. (1988) · Biochemistry · doi:10.1021/bi00405a042 · Cited by 625 · see citing works

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

Wang, S., Peng, J., Ma, J., & Xu, J. (2016) · Scientific Reports · doi:10.1038/srep18962 · Cited by 556 · see citing works

Amino Acid Properties and Consequences of Substitutions

Betts, M. J., & Russell, R. B. (2003) · doi:10.1002/0470867302.ch14 · Cited by 490 · see citing works

Automated assembly of protein blocks for database searching

Henikoff, S., & Henikoff, J. G. (1991) · Nucleic Acids Research · doi:10.1093/nar/19.23.6565 · Cited by 469 · see citing works

Amino‐Acid Properties and Consequences of Substitutions

Betts, M. J., & Russell, R. B. (2007) · doi:10.1002/9780470059180.ch13 · Cited by 452 · see citing works

Solving the protein sequence metric problem

Atchley, W. R., Zhao, J., Fernandes, A. D., & Drüke, T. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408677102 · Cited by 452 · see citing works

Medium- and Long-Range Interaction Parameters between Amino Acids for Predicting Three-Dimensional Structures of Proteins

Tanaka, S., & Scheraga, H. A. (1976) · Macromolecules · doi:10.1021/ma60054a013 · Cited by 437 · see citing works

Amino acid side chain parameters for correlation studies in biology and pharmacology

Fauchère, J., Charton, M., Kier, L. B., Verloop, A., & Pliška, V. (1988) · International journal of peptide & protein research · doi:10.1111/j.1399-3011.1988.tb01261.x · Cited by 386 · see citing works

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility

Heffernan, R., Yang, Y., Paliwal, K. K., & Zhou, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx218 · Cited by 358 · see citing works

Critical assessment of methods of protein structure prediction (CASP)—Round XII

Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25415 · Cited by 327 · see citing works

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

Liu, B. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx165 · Cited by 322 · see citing works

Learned protein embeddings for machine learning

Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty178 · Cited by 316 · see citing works

Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks

Meiler, J., Zeidler, A., Schm�schke, F., & M�ller, M. (2001) · Journal of Molecular Modeling · doi:10.1007/s008940100038 · Cited by 271 · see citing works

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Yang, Y., Gao, J., Wang, J., Heffernan, R., Hanson, J., Paliwal, K. K., & Zhou, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw129 · Cited by 237 · see citing works

Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?

Skolnick, J., Godzik, A., Jaroszewski, L., & Koliński, A. (1997) · Protein Science · doi:10.1002/pro.5560060317 · Cited by 201 · see citing works

DeepSF: deep convolutional neural network for mapping protein sequences to folds

Hou, J., Adhikari, B., & Cheng, J. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx780 · Cited by 179 · see citing works

Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection

Damoulas, T., & Girolami, M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn112 · Cited by 163 · see citing works

Improving Prediction of Protein Secondary Structure Using Structured Neural Networks and Multiple Sequence Alignments

Riis, S. K., & Krogh, A. (1996) · Journal of Computational Biology · doi:10.1089/cmb.1996.3.163 · Cited by 157 · see citing works

A comprehensive review and comparison of different computational methods for protein remote homology detection

Chen, J., Guo, M., Wang, X., & Liu, B. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw108 · Cited by 144 · see citing works

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

Liu, B., Wang, X., Lin, L., Dong, Q., & Wang, X. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-510 · Cited by 136 · see citing works

Review and comparative assessment of sequence-based predictors of protein-binding residues

Zhang, J., & Kurgan, L. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx022 · Cited by 114 · see citing works

Environment-dependent residue contact energies for proteins

Zhang, C., & Kim, S. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.040573597 · Cited by 114 · see citing works

Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural NetworksPreprint

Li, Z., & Yu, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.07176 · Cited by 107 · see citing works

New techniques for extracting features from protein sequences

Wang, J., Ma, Q., Shasha, D. E., & Wu, C. H. (2001) · IBM Systems Journal · doi:10.1147/sj.402.0426 · Cited by 107 · see citing works

Improved Protein Secondary Structure Prediction Using Support Vector Machine With a New Encoding Scheme and an Advanced Tertiary Classifier

Hu, H.-J., Pan, Y. F., Harrison, R. W., & Tai, P. C. (2004) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2004.837906 · Cited by 96 · see citing works

Protein structural alignments and functional genomics

Irving, J. A., Whisstock, J. C., & Lesk, A. M. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20010215)42:3 · Cited by 87 · see citing works

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

Asgari, E., McHardy, A. C., & Mofrad, M. R. K. (2019) · Scientific Reports · doi:10.1038/s41598-019-38746-w · Cited by 77 · see citing works

A study on protein sequence alignment quality

Elofsson, A. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10043 · Cited by 77 · see citing works

Comparative n-gram analysis of whole-genome protein sequences

Ganapathiraju, M. K., Weisser, D. K., Rosenfeld, R., Carbonell, J. G., Reddy, R., & Klein‐Seetharaman, J. (2002) · International Conference on Human Language Technology Research · doi:10.3115/1289189.1289259 · Cited by 66 · 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

Amino Acid Encoding Schemes from Protein Structure Alignments: Multi-dimensional Vectors to Describe Residue Types

Lin, K., May, A. C. W., & Taylor, W. R. (2002) · Journal of Theoretical Biology · doi:10.1006/jtbi.2001.2512 · Cited by 58 · see citing works

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway

Zhou, J., Wang, H., Zhao, Z., Xu, R., & Lu, Q. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2067-8 · Cited by 55 · see citing works

An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier

Xia, J., Peng, Z., Qi, D., Mu, H., & Yang, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw768 · Cited by 51 · see citing works

Machine Learning Techniques for Protein Secondary Structure Prediction:An Overview and Evaluation

Yoo, P. D., Zhou, B. B., & Zomaya, A. Y. (2008) · Current Bioinformatics · doi:10.2174/157489308784340676 · Cited by 47 · see citing works

A neural network model for the prediction of membrane‐spanning amino acid sequences

Lohmann, R., Schneider, G., Behrens, D., & Wrede, P. (1994) · Protein Science · doi:10.1002/pro.5560030924 · Cited by 46 · see citing works

CASP 11 target classification

Kinch, L. N., Li, W., Schaeffer, R. D., Dunbrack, R. L., Monastyrskyy, B., Kryshtafovych, A., & Grishin, N. V. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24982 · Cited by 36 · see citing works

PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction

Xu, Y., Song, J., Wilson, C., & Whisstock, J. C. (2018) · Scientific Reports · doi:10.1038/s41598-018-26392-7 · Cited by 36 · see citing works

Using a neural network to backtranslate amino acid sequences

White, G., & Seffens, W. S. (1998) · Electronic Journal of Biotechnology · doi:10.2225/vol1-issue3-fulltext-5 · Cited by 33 · see citing works

Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites

Jagla, B., & Schuchhardt, J. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.3.245 · Cited by 32 · see citing works

A large-scale comparative assessment of methods for residue–residue contact prediction

Wuyun, Q., Zheng, W. H., Peng, Z., & Yang, J. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw106 · Cited by 28 · see citing works

Mining for class-specific motifs in protein sequence classification

Srinivasan, S., Vural, S., King, B. R., & Guda, C. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-96 · Cited by 24 · see citing works

Deep Recurrent Conditional Random Field Network for Protein Secondary Prediction

Johansen, A. R., Sønderby, C. K., Sønderby, S. K., & Winther, O. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107489 · Cited by 23 · see citing works

Reaching optimized parameter set: protein secondary structure prediction using neural network

Dongardive, J., & Abraham, S. (2016) · Neural Computing and Applications · doi:10.1007/s00521-015-2150-2 · Cited by 16 · see citing works

Amino acid encoding schemes for machine learning methods

Zamani, M., & Kremer, S. C. (2011) · IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) · doi:10.1109/bibmw.2011.6112394 · Cited by 16 · see citing works

A novel knowledge-based approach to design inorganic-binding peptides

Oren, E., Tamerler, C., Sahin, D., Hnilova, M., Şeker, U., Sarikaya, M., & Samudrala, R. (2007) · Bioinform. · doi:10.1093/bioinformatics/btm436 · Cited by 10 · see citing works

Applications of nonlinear system identification to protein structural prediction

Robert, D. (2000) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 8 · see citing works

Parallel protein secondary structure prediction based on neural networks

Zhong, W., Altun, G., Tian, X., Harrison, R. W., Tai, P. C., & Pan, Y. F. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2004.1403842 · Cited by 5 · see citing works

A Review of Sensors and Biosensors Modified with Conducting Polymers and Molecularly Imprinted Polymers Used in Electrochemical Detection of Amino Acids: Phenylalanine, Tyrosine, and Tryptophan

Dinu, A., & Apetrei, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031218 · Cited by 84 · see citing works

Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery

Hsueh, H. T., Chou, R. T., Rai, U., Liyanage, W., Kim, Y. C., Appell, M. B., Pejavar, J., Leo, K. T., Davison, C., Kolodziejski, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38056-w · Cited by 81 · see citing works

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works

Sequence-based peptide identification, generation, and property prediction with deep learning: a review

Chen, X., Li, C., Bernards, M. T., Shi, Y., Shao, Q., & He, Y. (2021) · Molecular Systems Design & Engineering · doi:10.1039/d0me00161a · Cited by 56 · see citing works

Prediction of Protein–Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets

Xie, Z., Deng, X., & Shu, K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21020467 · Cited by 55 · see citing works

EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

Ge, R., Feng, G., Jing, X., Zhang, R., Wang, P., & Wu, Q. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00760 · Cited by 51 · see citing works

An analysis of protein language model embeddings for fold prediction

Villegas-Morcillo, A., Gomez, A. M., & Sanchez, V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac142 · Cited by 46 · see citing works

Deep learning for protein secondary structure prediction: Pre and post-AlphaFold

Ismi, D. P., Pulungan, R., & Afiahayati (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.012 · Cited by 53 · see citing works

Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction

Rio, A. L.-D., Martin, M. J., Perera-Lluna, A., & Saidi, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-71450-8 · Cited by 53 · see citing works

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-Specific Phosphorylation Sites

Ma, R., Li, S., Li, W., Yao, L., Huang, H., & Lee, T.-Y. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.06.004 · Cited by 43 · see citing works

Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques

Barbero-Aparicio, J. A., Olivares-Gil, A., Diez, J. J. R., García-Osorio, C., & Díez-Pastor, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102035 · Cited by 33 · see citing works

Machine learning in predictive biocatalysis: A comparative review of methods and applications

Tripathi, N., Hérisson, J., & Faulon, J. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108698 · Cited by 36 · see citing works

Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks

Bhattacharya, D., Kleeblatt, D. C., Statt, A., & Reinhart, W. F. (2022) · Soft Matter · doi:10.1039/d2sm00452f · Cited by 35 · see citing works

Recent developments in the preparation, characterization, and applications of chemosensors for environmental pollutants detection

Pundi, A., & Chang, C. (2023) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2023.110346 · Cited by 35 · see citing works

Artificial intelligence based methods for hot spot prediction

Ovek, D., Abali, Z., Zeylan, M. E., Keskin, Ö., Gürsoy, A., & Tunçbağ, N. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.003 · Cited by 36 · see citing works

A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction

Dhanuka, R., Singh, J. P., & Tripathi, A. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3247634 · Cited by 34 · see citing works

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Anteghini, M., dos Santos, V. A. P. M., & Saccenti, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22126409 · Cited by 30 · see citing works

Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites

Wang, X. R., Ding, Z., Wang, R., & Lin, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac631 · Cited by 33 · see citing works

ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides

Feng, G., Yao, H., Li, C., Liu, R., Huang, R., Fan, X., Ge, R., & Miao, Q. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105459 · Cited by 29 · see citing works

Finding functional motifs in protein sequences with deep learning and natural language models

Savojardo, C., Martelli, P. L., & Casadio, R. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102641 · Cited by 29 · see citing works

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning

Alakuş, T. B., & Türkoğlu, İ. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00405-4 · Cited by 28 · see citing works

Deep learning program to predict protein functions based on sequence information

Ko, C. W., Huh, J., & Park, J. (2022) · MethodsX · doi:10.1016/j.mex.2022.101622 · Cited by 21 · see citing works

A Semi-Supervised Autoencoder-Based Approach for Protein Function Prediction

Dhanuka, R., Tripathi, A., & Singh, J. P. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3163150 · Cited by 18 · see citing works

Application of Sequence Embedding in Protein Sequence-Based Predictions

Ibtehaz, N., & Kihara, D. (2022) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811258589_0002 · Cited by 16 · see citing works

ProstaNet: A Novel Geometric Vector Perceptrons–Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation

Liang, T., Sun, Z., Ishima, R., Xie, X., Xue, Y., Li, W., & Feng, Z. (2025) · Research · doi:10.34133/research.0674 · Cited by 14 · see citing works

An Innovative Bispectral Deep Learning Method for Protein Family Classification

Abu-Qasmieh, I. F., Fahoum, A. S. A., Alquran, H., & Zyout, A. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.037431 · Cited by 13 · see citing works

Single‐Molecule Classification of Aspartic Acid and Leucine by Molecular Recognition through Hydrogen Bonding and Time‐Series Analysis

Ryu, J., Komoto, Y., Ohshiro, T., & Taniguchi, M. (2022) · Chemistry - An Asian Journal · doi:10.1002/asia.202200179 · Cited by 13 · see citing works

DeepDRP: Prediction of intrinsically disordered regions based on integrated view deep learning architecture from transformer-enhanced and protein information

Yang, Z., Wang, Y., Ni, X., & Yang, S. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.127390 · Cited by 12 · see citing works

A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

Fahoum, A. S. A., Zyout, A., Alquran, H., & Abu-Qasmieh, I. F. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038304 · Cited by 11 · see citing works

Deep learning methods for protein representation and function prediction: A comprehensive overview

Wang, M., Nie, Z., He, Y., Vasilakos, A. V., Cheng, Q. S., & Ren, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110977 · Cited by 11 · see citing works

DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

Cao, L., Zhang, Q., Song, H., Lin, K., & Pang, E. (2022) · iScience · doi:10.1016/j.isci.2022.105345 · Cited by 11 · see citing works

Comparative study of encoded and alignment-based methods for virus taxonomy classification

Shaukat, M. A., Nguyen, T. T., Hsu, E. B., Yang, S. S., & Bhatti, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-45461-0 · Cited by 10 · see citing works

A systematic review on the state-of-the-art strategies for protein representation

Yue, Z., Yan, T., Xu, H., Liu, Y., Hong, Y., Chen, G.-X., Xie, T., & Tao, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106440 · Cited by 10 · see citing works

A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning

Alquran, H., Fahoum, A. S. A., Zyout, A., & Abu-Qasmieh, I. F. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295805 · Cited by 10 · see citing works

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineeringPreprint

Illig, A.-M., Siedhoff, N. E., Schwaneberg, U., & Davari, M. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.07.495081 · Cited by 10 · see citing works

Network Intrusion Detection Based on Amino Acid Sequence Structure Using Machine Learning

Ibaisi, T. A., Kühn, S., Kaiiali, M., & Kazim, M. (2023) · Electronics · doi:10.3390/electronics12204294 · Cited by 13 · see citing works

FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis

Li, W., Yang, L., Qiu, Y., Yuan, Y., Li, X., & Meng, Z. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04889-3 · Cited by 8 · see citing works

Machine learning approaches for predicting protein-ligand binding sites from sequence data

Vural, O., & Jololian, L. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1520382 · Cited by 8 · see citing works

MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework

Chen, S., Li, M., & Semenov, I. (2024) · Methods · doi:10.1016/j.ymeth.2024.02.008 · Cited by 7 · see citing works

How did we get there? AI applications to biological networks and sequences

Anteghini, M., Gualdi, F., & Oliva, B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110064 · Cited by 9 · see citing works

A privacy encryption scheme for medical images based on nonlinear dynamic coupled map lattices and protein sequence

Wang, G.-P., Ye, X.-L., & Zhou, S. (2024) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-024-05415-5 · Cited by 4 · see citing works

A multimodal model for protein function prediction

Mao, Y., Xu, W., Shun, Y., Chai, L., Xue, L., Yang, Y., & Li, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-94612-y · Cited by 6 · see citing works

HybAVPnet: A Novel Hybrid Network Architecture for Antiviral Peptides Prediction

Ge, R., Xia, Y., Jiang, M., Jia, G., Jing, X., Li, Y., & Cai, Y. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3385635 · Cited by 6 · see citing works

HybAVPnet: a novel hybrid network architecture for antiviral peptides identificationPreprint

Ge, R., Xia, Y., Jiang, M., Jia, G., Jing, X., Li, Y., & Cai, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.10.495721 · Cited by 6 · see citing works

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201

Jiang, S., Su, Z., Bloodworth, N. C., Liu, Y., Martina, C. E., Harrison, D. G., & Meiler, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0314833 · Cited by 5 · see citing works

Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences

Agrawal, S., Sisodia, D. S., & Nagwani, N. K. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02436-5 · Cited by 5 · see citing works

Protein function prediction using functional inter-relationship

Dhanuka, R., & Singh, J. P. (2021) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2021.107593 · Cited by 5 · see citing works

A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites

Wang, S., Zhang, L., Yang, R., & Zhao, Y. (2022) · Biomolecules · doi:10.3390/biom12121854 · Cited by 5 · see citing works

A novel entropy-based mapping method for determining the protein-protein interactions in viral genomes by using coevolution analysis

Alakuş, T. B., & Türkoğlu, İ. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102359 · Cited by 5 · see citing works

PhosHSGN: Deep Neural Networks Combining Sequence and Protein Spatial Information to Improve Protein Phosphorylation Site Prediction

Lu, J., Chen, H., & Qiu, J. (2024) · IEEE Access · doi:10.1109/access.2024.3427792 · Cited by 4 · see citing works

DeepPTMPred: a multi-modal deep learning framework for accurate prediction of protein post-translational modification sites

Wang, C., Jiang, Q., Yu, D., Guan, J., Chen, Z., Lu, X., Yan, B., Qin, J., Liu, Y., & Wang, J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag321 · Cited by 4 · see citing works

A Comparative Study of Amino Acid Encoding Methods for Predicting Drug-Target Interactions in COVID-19 Disease

Alakuş, T. B., & Türkoğlu, İ. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-72834-2_18 · Cited by 4 · see citing works

Protein sequence analysis in the context of drug repurposing

Sánchez, N. G., Carro, E. U., Santamaría, L. P., & Rodríguez‐González, A. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02531-1 · Cited by 5 · see citing works

Using Chou’s 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods

Zhang, Z., & Wang, L. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1821778 · Cited by 4 · see citing works

2020 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 17

(2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3047571 · Cited by 4 · see citing works

Multiview and Decision Fusion in Stacking Ensemble To Predict Anti-Cancer Peptides

Kumar, A., & Singh, D. (2024) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon60325.2024.10687578 · Cited by 3 · see citing works

i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification

Jiang, M., Zhang, R., Xia, Y., Jia, G., Yin, Y., Wang, P., Wu, J., & Ge, R. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.884589 · Cited by 3 · see citing works

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort

Illig, A.-M., Siedhoff, N. E., Davari, M. D., & Schwaneberg, U. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00704 · Cited by 3 · see citing works

MCTASmRNA: A deep learning framework for alternative splicing events classification

Zheng, J.-Y., Gao, J., Gao, F.-H., Ren, S.-N., Zhu, C., Xie, J., Li, Z., Yin, W., Xia, X., Li, Y., et al. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.139941 · Cited by 3 · see citing works

Multi-function Prediction of Unknown Protein Sequences Using Multilabel Classifiers and Augmented Sequence Features

Agrawal, S., Sisodia, D. S., & Nagwani, N. K. (2021) · Iranian Journal of Science and Technology Transactions A Science · doi:10.1007/s40995-021-01134-z · Cited by 3 · see citing works

Artificial intelligence-driven protease cleavage site prediction: Advances and challenges

Yang, L., Ji, Z., Liu, C., Fan, H., Yang, X., & Shi, C. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108867 · Cited by 2 · see citing works

WGAN-GP_Glu: A semi-supervised model based on double generator-Wasserstein GAN with gradient penalty algorithm for glutarylation site identification

Qiao, N., & Qi, Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109328 · Cited by 2 · see citing works

MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction

Zhu, Y.-H., Zhu, S., Yu, X., Yan, H., Liu, Y., Xie, X., Yu, D., & Ye, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf420 · Cited by 1 · see citing works

Contrastive representation learning and capsule networks enable accurate identification of ferroptosis-related proteins

Zhao, Y., Liu, X., Xie, P., Guan, J., Zhao, Z., Wang, J., Lee, T., Chiang, Y.-C., Wei, L., Liu, X., et al. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01183-9 · Cited by 1 · see citing works

Interpretable convolutional neural networks for sequence-based classification and discovery of plastic-degrading enzymes

Lee, W.-H., Dumontet, L., Jung, K., Lee, H. J., Thapa, G., Oh, T., & Kang, M. (2026) · Applied and Environmental Microbiology · doi:10.1128/aem.01586-25 · Cited by 2 · see citing works

GLTM: A Global-Local Attention LSTM Model to Locate Dimer Motif of Single-Pass Membrane Proteins

Ma, Q., Zou, K., Zhang, Z., & Yang, F. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.854571 · Cited by 1 · see citing works

Phylogenetic Analysis: A Novel Method of Protein Sequence Similarity Analysis

Li, W., Yang, L., Meng, Z., Qiu, Y., Wang, P. S. P., & Li, X. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422580071 · Cited by 1 · see citing works

Prediction of human protein functions with protein mapping techniques and deep learning model

Alakuş, T. B., & Türkoğlu, İ. (2022) · Pamukkale University Journal of Engineering Sciences · doi:10.5505/pajes.2021.51261 · Cited by 1 · see citing works

High-precision Protein Sequence Comparison Based on Principal Component Analysis and Discrete Wavelet Transform

Li, W., Yang, L., & Meng, Z. (2021) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait53529.2021.9731147 · Cited by 1 · see citing works

RAANMF: An adaptive sequence feature representation method for predictions of protein thermostability, PPI, and drug–target interaction

Yan, Q., Pan, S., Cheng, Z., & Ding, Y. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107819 · Cited by 1 · see citing works

Bi-level deep feature synthesis through variational autoencoder for predicting peptide antiviral activity using optimized deep ensemble

Kumar, A., & Singh, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128093 · Cited by 1 · see citing works

A Method to Generate Complex Predictive Features for ML-Based Prediction of the Local Protein Structure

Milchevskiy, Y. V., Milchevskaya, V., & Kravatsky, Y. V. (2023) · Molecular Biology · doi:10.31857/s0026898423010093 · Cited by 1 · see citing works

Method to Generate Complex Predictive Features for Machine Learning-Based Prediction of the Local Structure and Functions of Proteins

Milchevskiy, Y. V., Milchevskaya, V., & Kravatsky, Y. V. (2023) · Molecular Biology · doi:10.1134/s0026893323010089 · Cited by 1 · see citing works

Effect of Sequence Padding on the Performance of Protein-Based Deep Learning ModelsPreprint

Rio, A. L.-D., Martin, M. J., Perera-Lluna, A., & Saidi, R. (2020) · doi:10.21203/rs.2.21336/v1 · Cited by 2 · see citing works

AI predicted TCR-pMHC structures differentiate immune interactionsPreprint

Robben, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.24.707744 · Cited by 1 · see citing works

ProFPred: a two-step protein function prediction model based on sequence and evolutionary information

Ge, R., Feng, G., Wang, P., & Miao, Q. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313358

GLMFD: An Attention-Based CNN-LSTM Model for Transmembrane Domains Localization

Ma, Q., Yang, F., Zou, K., & Zhang, Z. (2022) · International Conference on Electronic Communication and Artificial Intelligence (ICECAI) · doi:10.1109/iwecai55315.2022.00098

Predicting protein intrinsically disordered regions by applying natural language processing practices

Chakraborty, R., & Hasija, Y. (2022) · Soft Computing · doi:10.1007/s00500-022-07085-w

Analysis of proteomes—III

Mir, R. A., Shafi, S. M., & Zargar, S. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99045-5.00006-9

REC protein family expansion by the emergence of a new signaling pathway

Garber, M. E., Frank, V., Kazakov, A. E., Incha, M. R., Nava, A. A., Zhang, H., Valencia, L. E., Keasling, J. D., Rajeev, L., & Mukhopadhyay, A. (2023) · mBio · doi:10.1128/mbio.02622-23

MehNet: a vigesimal-based model by amino acid melting points generates unique ID numbers for protein sequences

Erten, M. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2302937

CRULoc: A protein subcellular localization prediction model based on BiGRU and CNN

Lu, Y. (2024) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat62382.2024.10695442

Multiview Learning and Decision Fusion Based Ensemble to Predict Anti-Cancer Peptide

Kumar, A., & Singh, D. (2024) · International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT) · doi:10.1109/ic2sdt62152.2024.10695980

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201Preprint

Jiang, S., Su, Z., Bloodworth, N. C., Liu, Y., Martina, C. E., Harrison, D. G., & Meiler, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.19.624425

Prediction of Protein Secondary Structure Using a Hybrid Convolutional Blocks with GRU Units

Yang, S., & Chen, X. (2024) · Frontiers in Computing and Intelligent Systems · doi:10.54097/k8rmxp27

Machine learning-driven identification of virulence determinants in Borrelia burgdorferi associated with human dissemination

Nguyen, H. T., & Brissette, C. A. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014407

Plastic-hydrolytic enzyme classification using explainable deep learningPreprint

Lee, W.-H., Dumontet, L., Jung, K., Lee, H. J., Thapa, G., Oh, T., & Kang, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.14.664602

Additional file 1 of Protein sequence analysis in the context of drug repurposing

Sánchez, N. G., Carro, E. U., Prieto-Santamaría, L., & Rodríguez‐González, A. (2024) · Figshare · doi:10.6084/m9.figshare.26719884

Pediocin from Local Pediococcus pentosaceus Strains: Antimicrobial Potential, Amino Acid Properties, and Sustainable Alternatives to Antibiotics

Alsaraaj, E. G., Ibrahim, T. Z., & Alomari, A. (2025) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1549/1/012162

SeqDPPIV: A Sequence-Based Model for Predicting Dipeptidyl Peptidase-IV Inhibitory Peptides Using Simple Amino Acid Descriptors and CatBoost Classifier

Balachandran, P., & Bhadra, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13806-4_6

What Kinds of Machine Learning Models Might Guide the Design of Biomedical Nanomaterials?

Wang, X., Lin, X., Wu, T., Sun, Y., Guan, Y., Mao, C., & Wan, M. (2026) · ACS Nano Medicine · doi:10.1021/acsnanomed.5c00112

Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns

Serage, R., Nyirenda, C., Omomule, T. G., Christoffels, A. G., & Anderson, D. E. (2026) · Bioinformatics and Biology Insights · doi:10.1177/11779322251415123

Artificial Intelligence Applications in Macromolecular Research: A Comprehensive Review

Rani, L., Sarangi, P. K., Sahoo, A. K., & Agrawal, A. K. (2026) · Macromolecular Symposia · doi:10.1002/masy.70230

Leveraging Residual Graph Convolutional Networks with Cross-Attention Mechanisms for High-Accuracy Protein Function Prediction

Li, P., Wang, W., & Yu, D.-J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00101

Accurate proteome-wide prediction of enzymes and catalytic sites using graph deep learning and protein language model

Ng, Y.-L., Wang, X., Li, Y., Huang, J., Lai, H. M., Chan, J. Y. K., NG, B. K. W., Ko, H., & Kwok, K. (2026) · GigaScience · doi:10.1093/gigascience/giag056

An optimized multi-view deep ensemble for identifying anti-viral and anti-corona peptides

Kumar, A., & Singh, D. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115523

Carbamate post-translational modifications: bridging CO2 biology and protein regulation

Dewhurst, R. M., Weston, G. M. I., & Cann, M. J. (2026) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2026.09.014

Application and Research of Machine Leaning Based Protein Sequence Encoding

Zhou, W. (2008) · Computer Knowledge and Technology

Protein secondary structure prediction using support vector machines and a codon encoding scheme

Zamani, M., & Kremer, S. C. (2012) · IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) · doi:10.1109/bibmw.2012.6470326 · Cited by 7 · see citing works

Influence of encoding scheme on protein secondary structure prediction

Zou, D., He, Z., He, J., & Huang, X. (2008) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2008.4593133

Machine Learning Techniques for Protein Secondary Structure Prediction:An Overview and Evaluation

Yoo, P. D., Zhou, B. B., & Zomaya, A. Y. (2008) · Current Bioinformatics · doi:10.2174/157489308784340676 · Cited by 47 · see citing works

Protein structure prediction from predicted residue properties utilizing a digital encoding algorithm

Gilbert, R. J. C. (1992) · Journal of Molecular Graphics · doi:10.1016/0263-7855(92)80064-k · Cited by 11 · see citing works