{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T11:55:58Z","timestamp":1782215758139,"version":"3.54.5"},"reference-count":75,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:00:00Z","timestamp":1734739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Scientific Foundation of China","award":["62402089"],"award-info":[{"award-number":["62402089"]}]},{"name":"National Nature Scientific Foundation of China","award":["25QNJJ2951"],"award-info":[{"award-number":["25QNJJ2951"]}]},{"name":"National Nature Scientific Foundation of China","award":["2023TQ0047"],"award-info":[{"award-number":["2023TQ0047"]}]},{"name":"National Nature Scientific Foundation of China","award":["GZC20230380"],"award-info":[{"award-number":["GZC20230380"]}]},{"name":"Sichuan Provincial Youth Fund Project","award":["62402089"],"award-info":[{"award-number":["62402089"]}]},{"name":"Sichuan Provincial Youth Fund Project","award":["25QNJJ2951"],"award-info":[{"award-number":["25QNJJ2951"]}]},{"name":"Sichuan Provincial Youth Fund Project","award":["2023TQ0047"],"award-info":[{"award-number":["2023TQ0047"]}]},{"name":"Sichuan Provincial Youth Fund Project","award":["GZC20230380"],"award-info":[{"award-number":["GZC20230380"]}]},{"name":"China Postdoctoral Science Foundation","award":["62402089"],"award-info":[{"award-number":["62402089"]}]},{"name":"China Postdoctoral Science Foundation","award":["25QNJJ2951"],"award-info":[{"award-number":["25QNJJ2951"]}]},{"name":"China Postdoctoral Science Foundation","award":["2023TQ0047"],"award-info":[{"award-number":["2023TQ0047"]}]},{"name":"China Postdoctoral Science Foundation","award":["GZC20230380"],"award-info":[{"award-number":["GZC20230380"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large coronavirus family with high infectivity and pathogenicity and is the primary pathogen causing the global pandemic of coronavirus disease 2019 (COVID-19). Phosphorylation is a major type of protein post-translational modification that plays an essential role in the process of SARS-CoV-2\u2013host interactions. The precise identification of phosphorylation sites in host cells infected with SARS-CoV-2 will be of great importance to investigate potential antiviral responses and mechanisms and exploit novel targets for therapeutic development. Numerous computational tools have been developed on the basis of phosphoproteomic data generated by mass spectrometry-based experimental techniques, with which phosphorylation sites can be accurately ascertained across the whole SARS-CoV-2-infected proteomes. In this work, we have comprehensively reviewed several major aspects of the construction strategies and availability of these predictors, including benchmark dataset preparation, feature extraction and refinement methods, machine learning algorithms and deep learning architectures, model evaluation approaches and metrics, and publicly available web servers and packages. We have highlighted and compared the prediction performance of each tool on the independent serine\/threonine (S\/T) and tyrosine (Y) phosphorylation datasets and discussed the overall limitations of current existing predictors. In summary, this review would provide pertinent insights into the exploitation of new powerful phosphorylation site identification tools, facilitate the localization of more suitable target molecules for experimental verification, and contribute to the development of antiviral therapies.<\/jats:p>","DOI":"10.3390\/ijms252413674","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T02:39:56Z","timestamp":1734921596000},"page":"13674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection"],"prefix":"10.3390","volume":"25","author":[{"given":"Hongyan","family":"Lai","sequence":"first","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijia","family":"Xie","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwei","family":"Luo","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feitong","family":"Hong","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diyu","family":"Luo","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5285-6044","authenticated-orcid":false,"given":"Fuying","family":"Dao","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6265-2862","authenticated-orcid":false,"given":"Hao","family":"Lin","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4487-2727","authenticated-orcid":false,"given":"Kunxian","family":"Shu","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Lv","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, B., Guo, H., Zhou, P., and Shi, Z.-L. (2020). Characteristics of SARS-CoV-2 and COVID-19. Nat. Rev. Microbiol., 19.","DOI":"10.1038\/s41579-020-00459-7"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study","volume":"395","author":"Chen","year":"2020","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1002\/imt2.42","article-title":"TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms","volume":"1","author":"Ren","year":"2022","journal-title":"iMETA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s00018-020-03594-9","article-title":"Multi-substrate selectivity based on key loops and non-homologous domains: New insight into ALKBH family","volume":"78","author":"Xu","year":"2021","journal-title":"Cell. Mol. Life Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1038\/s41586-021-03493-4","article-title":"Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV","volume":"594","author":"Stukalov","year":"2021","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.cell.2020.06.034","article-title":"The Global Phosphorylation Landscape of SARS-CoV-2 Infection","volume":"182","author":"Bouhaddou","year":"2020","journal-title":"Cell"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1038\/s41586-020-2286-9","article-title":"A SARS-CoV-2 protein interaction map reveals targets for drug repurposing","volume":"583","author":"Gordon","year":"2020","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/s41586-023-06422-9","article-title":"Dissecting human population variation in single-cell responses to SARS-CoV-2","volume":"621","author":"Aquino","year":"2023","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1093\/bioinformatics\/btz694","article-title":"Identifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism","volume":"36","author":"Hong","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.omtn.2019.04.019","article-title":"Meta-4mCpred: A sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation","volume":"16","author":"Manavalan","year":"2019","journal-title":"Mol. Ther. Nucleic Acids"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1093\/bib\/bby053","article-title":"Function determinants of TET proteins: The arrangements of sequence motifs with specific codes","volume":"20","author":"Liu","year":"2019","journal-title":"Brief. Bioinform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/s41392-022-01239-w","article-title":"SARS-CoV-2 hijacks cellular kinase CDK2 to promote viral RNA synthesis","volume":"7","author":"Guo","year":"2022","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, C., Feng, F., Shi, Y., Li, W., Wang, Z., Zhu, Y., Yuan, S., Hu, D., Dai, J., and Jiang, Q. (2022). Protein Kinase C Inhibitors Reduce SARS-CoV-2 Replication in Cultured Cells. Microbiol. Spectr., 10.","DOI":"10.1128\/spectrum.01056-22"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Naik, R.R., Shakya, A.K., Aladwan, S.M., and El-Tanani, M. (2022). Kinase Inhibitors as Potential Therapeutic Agents in the Treatment of COVID-19. Front. Pharmacol., 13.","DOI":"10.3389\/fphar.2022.806568"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, L., He, W., Malik, A., Su, R., Cui, L., and Manavalan, B. (2020). Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework. Brief. Bioinform., 22.","DOI":"10.1093\/bib\/bbaa275"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ai, C., Yang, H., Liu, X., Dong, R., Ding, Y., and Guo, F. (2024). MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning. PLoS Comput. Biol., 20.","DOI":"10.1371\/journal.pcbi.1012229"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2400009","DOI":"10.1002\/advs.202400009","article-title":"CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs","volume":"11","author":"Liu","year":"2024","journal-title":"Adv. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s13321-023-00721-z","article-title":"DeepAR: A novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists","volume":"15","author":"Schaduangrat","year":"2023","journal-title":"J. Cheminformatics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/nar\/gkad055","article-title":"DeepBIO: An automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis","volume":"51","author":"Wang","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1093\/bioinformatics\/bty1047","article-title":"mAHTPred: A sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation","volume":"35","author":"Manayalan","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, T., Huang, J., Luo, D., Ren, L., Ning, L., Huang, J., Lin, H., and Zhang, Y. (2024). Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy. Int. J. Biol. Macromol., 264.","DOI":"10.1016\/j.ijbiomac.2024.130638"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"212106","DOI":"10.1007\/s11432-024-4171-9","article-title":"SBSM-Pro: Support bio-sequence machine for proteins","volume":"67","author":"Wang","year":"2024","journal-title":"Sci. China Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arif, M., Musleh, S., Fida, H., and Alam, T. (2024). PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-67433-8"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ren, L., Ning, L., Yang, Y., Yang, T., Li, X., Tan, S., Ge, P., Li, S., Luo, N., and Tao, P. (2023). MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19. Comput. Biol. Med., 167.","DOI":"10.1016\/j.compbiomed.2023.107661"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.future.2024.06.008","article-title":"ACVPred: Enhanced prediction of anti-coronavirus peptides by transfer learning combined with data augmentation","volume":"160","author":"Xu","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lv, H., Dao, F.Y., Zulfiqar, H., and Lin, H. (2021). DeepIPs: Comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach. Brief. Bioinform., 22.","DOI":"10.1093\/bib\/bbab244"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pham, N.T., Phan, L.T., Seo, J., Kim, Y., Song, M., Lee, S., Jeon, Y.-J., and Manavalan, B. (2023). Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach. Brief. Bioinform., 25.","DOI":"10.1093\/bib\/bbad433"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: Accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"131848","DOI":"10.1016\/j.foodchem.2021.131848","article-title":"Improved yield of theaflavin-3, 3\u02b9-digallate from Bacillus megaterium tyrosinase via directed evolution","volume":"375","author":"Zhou","year":"2022","journal-title":"Food Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.1093\/nar\/gkz740","article-title":"BioSeq-Analysis2.0: An updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches","volume":"47","author":"Liu","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, H., and Liu, B. (2023). BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo. PLOS Comput. Biol., 19.","DOI":"10.1371\/journal.pcbi.1011214"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Charoenkwan, P., Schaduangrat, N., and Shoombuatong, W. (2023). StackTTCA: A stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. BMC Bioinform., 24.","DOI":"10.1186\/s12859-023-05421-x"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shoombuatong, W., Homdee, N., Schaduangrat, N., and Chumnanpuen, P. (2024). Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-55160-z"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ymeth.2024.08.003","article-title":"Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework","volume":"230","author":"Shoombuatong","year":"2024","journal-title":"Methods"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, T., Chen, J.M., Zhang, D., Zhang, Q., Peng, B., Xu, L., and Tang, H. (2020). ApoPred: Identification of Apolipoproteins and Their Subfamilies with Multifarious Features. Front. Cell Dev. Biol., 8.","DOI":"10.3389\/fcell.2020.621144"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/nar\/28.1.374","article-title":"AAindex: Amino acid index database","volume":"28","author":"Kawashima","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"baz131","DOI":"10.1093\/database\/baz131","article-title":"RAACBook: A web server of reduced amino acid alphabet for sequence-dependent inference by using Chou\u2019s five-step rule","volume":"2019","author":"Zheng","year":"2019","journal-title":"Database"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8700","DOI":"10.1073\/pnas.92.19.8700","article-title":"Prediction of protein folding class using global description of amino acid sequence","volume":"92","author":"Dubchak","year":"1995","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1093\/bioinformatics\/btw564","article-title":"PseKRAAC: A flexible web server for generating pseudo K-tuple reduced amino acids composition","volume":"33","author":"Zuo","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gu, Z.-F., Hao, Y.-D., Wang, T.-Y., Cai, P.-L., Zhang, Y., Deng, K.-J., Lin, H., and Lv, H. (2024). Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur. BMC Biol., 22.","DOI":"10.1186\/s12915-024-01883-4"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ymeth.2024.04.004","article-title":"DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery","volume":"226","author":"Ali","year":"2024","journal-title":"Methods"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1006\/bbrc.2000.3815","article-title":"Prediction of protein subcellular locations by incorporating quasi-sequence-order effect","volume":"278","author":"Chou","year":"2000","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1126\/science.185.4154.862","article-title":"Amino acid difference formula to help explain protein evolution","volume":"185","author":"Grantham","year":"1974","journal-title":"Science"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0006-3495(94)80782-9","article-title":"The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: De novo design of an idealized leader peptidase cleavage site","volume":"66","author":"Schneider","year":"1994","journal-title":"Biophys. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"W434","DOI":"10.1093\/nar\/gkac351","article-title":"iFeatureOmega: An integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets","volume":"50","author":"Chen","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"W633","DOI":"10.1093\/nar\/gkac415","article-title":"RaacFold: A webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets","volume":"50","author":"Zheng","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, S., Liu, D., Song, Y., Liang, Y., Yu, H., and Zuo, Y. (2024). Designing a structure-function alphabet of helix based on reduced amino acid clusters. Arch. Biochem. Biophys., 754.","DOI":"10.1016\/j.abb.2024.109942"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1021\/jm9700575","article-title":"New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids","volume":"41","author":"Sandberg","year":"1998","journal-title":"J. Med. Chem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"10915","DOI":"10.1073\/pnas.89.22.10915","article-title":"Amino acid substitution matrices from protein blocks","volume":"89","author":"Henikoff","year":"1992","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1093\/nar\/gkg600","article-title":"SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence","volume":"31","author":"Cai","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/07391102.2016.1163294","article-title":"OH-PRED: Prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids","volume":"35","author":"Jia","year":"2017","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"10410","DOI":"10.3390\/ijms150610410","article-title":"Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou\u2019s pseudo amino acid composition","volume":"15","author":"Jia","year":"2014","journal-title":"Int. J. Mol. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, S., Cui, C., Chen, H., and Liu, T. (2022). Ensemble learning-based feature selection for phosphorylation site detection. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.984068"},{"key":"ref_54","first-page":"8439","article-title":"MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier","volume":"9","author":"Zhang","year":"2024","journal-title":"ACS Omega"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymeth.2024.04.021","article-title":"Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies","volume":"229","author":"Sabir","year":"2024","journal-title":"Methods"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"100794","DOI":"10.1016\/j.xcrm.2022.100794","article-title":"Deep generative molecular design reshapes drug discovery","volume":"4","author":"Zeng","year":"2022","journal-title":"Cell Rep. Med."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, S. (2024). Decoding Medical Diagnosis with Machine Learning Classifiers. Medinformatics.","DOI":"10.47852\/bonviewMEDIN42022583"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3762","DOI":"10.1109\/JBHI.2024.3377362","article-title":"PTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings","volume":"28","author":"Xu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Huang, G., Xiao, R., Chen, W., and Dai, Q. (2024). GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection. Biology, 13.","DOI":"10.3390\/biology13100798"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e11","DOI":"10.1002\/imt2.11","article-title":"DeepKla: An attention mechanism-based deep neural network for protein lysine lactylation site prediction","volume":"1","author":"Lv","year":"2022","journal-title":"iMeta"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jiao, S., Ye, X., Ao, C., Sakurai, T., Zou, Q., and Xu, L. (2023). Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad627"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ymeth.2024.08.004","article-title":"PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells","volume":"230","author":"Li","year":"2024","journal-title":"Methods"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"102388","DOI":"10.1016\/j.jocs.2024.102388","article-title":"IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design","volume":"81","author":"Ali","year":"2024","journal-title":"J. Comput. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07391102.2024.2323144","article-title":"Dynamics, Deep-VEGF: Deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network","volume":"42","author":"Alsini","year":"2024","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"102448","DOI":"10.1016\/j.jocs.2024.102448","article-title":"VEGF-ERCNN: A Deep Learning-based Model for Prediction of Vascular Endothelial Growth Factor using Ensemble Residual CNN","volume":"83","author":"Ali","year":"2024","journal-title":"J. Comput. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhu, H., Hao, H., and Yu, L. (2023). Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. BMC Biol., 21.","DOI":"10.1186\/s12915-023-01796-8"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Manavalan, B., and Lee, J. (2022). FRTpred: A novel approach for accurate prediction of protein folding rate and type. Comput. Biol. Med., 149.","DOI":"10.1016\/j.compbiomed.2022.105911"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Nithiyanandam, S., Sangaraju, V.K., Manavalan, B., and Lee, G. (2023). Computational prediction of protein folding rate using structural parameters and network centrality measures. Comput. Biol. Med., 155.","DOI":"10.1016\/j.compbiomed.2022.106436"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.omtn.2023.02.027","article-title":"IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection","volume":"32","author":"Zhang","year":"2023","journal-title":"Mol. Ther. Nucleic Acids"},{"key":"ref_70","unstructured":"Chollet, F. (2018). Keras: The Python Deep Learning library. Astrophys. Source Code Libr., ascl:1806.022."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"239","DOI":"10.2174\/0115665232268074231026111634","article-title":"Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development","volume":"24","author":"Li","year":"2024","journal-title":"Curr. Gene Ther."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wang, M., Yan, L., Jia, J., Lai, J., Zhou, H., and Yu, B. (2023). DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanism. Comput. Biol. Med., 160.","DOI":"10.1016\/j.compbiomed.2023.106935"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wang, M., Jia, J., Xu, F., Zhou, H., Liu, Y., and Yu, B. (2024). Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network. Comput. Biol. Chem., 112.","DOI":"10.1016\/j.compbiolchem.2024.108183"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","article-title":"Model selection and estimation in regression with grouped variables","volume":"68","author":"Yuan","year":"2006","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Huang, Z., Chen, S., and Yu, L. (2023). Predicting new drug indications based on double variational autoencoders. Comput. Biol. Med., 164.","DOI":"10.1016\/j.compbiomed.2023.107261"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/25\/24\/13674\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:57:21Z","timestamp":1760115441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/25\/24\/13674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,21]]},"references-count":75,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["ijms252413674"],"URL":"https:\/\/doi.org\/10.3390\/ijms252413674","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,21]]}}}