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engine","volume":"28","author":"K\u00f6ster","year":"2012","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.eswa.2026.132559_b0370","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","article-title":"DeepGOPlus: Improved protein function prediction from sequence","volume":"36","author":"Kulmanov","year":"2020","journal-title":"Bioinformatics"},{"issue":"4","key":"10.1016\/j.eswa.2026.132559_b0375","doi-asserted-by":"crossref","first-page":"2672","DOI":"10.53759\/7669\/jmc202505205","article-title":"Fuzzy Logic Driven Intelligent System for uncertainty Aware Decision support using Heterogeneous Data","volume":"5","author":"Kumar","year":"2025","journal-title":"Journal of Machine and Computing"},{"key":"10.1016\/j.eswa.2026.132559_b0380","doi-asserted-by":"crossref","unstructured":"Kuriata, A., Iglesias, V., Pujols, J., Kurcinski, M., Kmiecik, S., & Ventura, S. (2019). Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility. Nucleic Acids Research, 7(W1), W300\u2013W307. doi:10.1093\/nar\/gkz321.","DOI":"10.1093\/nar\/gkz321"},{"key":"10.1016\/j.eswa.2026.132559_b0385","article-title":"Modern deep learning in bioinformatics","volume":"823\u2013827","author":"Li","year":"2022","journal-title":"Journal of molecular cell biology"},{"key":"10.1016\/j.eswa.2026.132559_b0390","doi-asserted-by":"crossref","first-page":"3464","DOI":"10.1016\/j.csbj.2025.07.045","article-title":"ProT-GFDM: A generative fractional diffusion model for protein generation","volume":"27","author":"Liang","year":"2025","journal-title":"Computational and Structural Biotechnology Journal"},{"issue":"102789","key":"10.1016\/j.eswa.2026.132559_b0395","article-title":"Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches","volume":"85","author":"Lin","year":"2024","journal-title":"Current opinion in structural biology"},{"key":"10.1016\/j.eswa.2026.132559_b0400","unstructured":"Lin, Y., & AlQuraishi, M. (2023). Generating novel, designable, and diverse protein structures by equivariantly diffusing oriented residue clouds. arXiv, preprint arXiv:2301.12485. doi:10.48550\/arXiv.2301.12485."},{"issue":"6637","key":"10.1016\/j.eswa.2026.132559_b0405","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Language models of protein sequences at the scale of evolution enable accurate structure prediction","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"issue":"6637","key":"10.1016\/j.eswa.2026.132559_b0410","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"issue":"3","key":"10.1016\/j.eswa.2026.132559_b0415","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/3236386.3241340","article-title":"The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery","volume":"16","author":"Lipton","year":"2018","journal-title":"Queue"},{"issue":"7","key":"10.1016\/j.eswa.2026.132559_b0420","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.1016\/j.apsb.2024.03.002","article-title":"In silico off-target profiling for enhanced drug safety assessment","volume":"14","author":"Liu","year":"2024","journal-title":"Acta Pharmaceutica Sinica B"},{"key":"10.1016\/j.eswa.2026.132559_b0425","doi-asserted-by":"crossref","unstructured":"Liu, J., Neupane, P., & Cheng, J. (2025). Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16. bioRxiv, 2025-06. doi:10.1101\/2025.06.06.658338.","DOI":"10.1101\/2025.06.06.658338"},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0430","article-title":"A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19","volume":"17","author":"Lupei","year":"2022","journal-title":"PLoS One1"},{"key":"10.1016\/j.eswa.2026.132559_b0435","article-title":"A multimodal model for protein function prediction","volume":"10465","author":"Mao","year":"2025","journal-title":"Scientific Reports"},{"issue":"D1","key":"10.1016\/j.eswa.2026.132559_b0440","doi-asserted-by":"crossref","first-page":"D222","DOI":"10.1093\/nar\/gku1221","article-title":"CDD: NCBI's conserved domain database","volume":"43","author":"Marchler-Bauer","year":"2015","journal-title":"Nucleic Acids Research"},{"key":"10.1016\/j.eswa.2026.132559_b0445","first-page":"29287","article-title":"Language models enable zero-shot prediction of the effects of mutations on protein function","volume":"34","author":"Meier","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.1016\/j.eswa.2026.132559_b0450","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.cct.2014.01.005","article-title":"The Effectiveness and Ineffectiveness of complex Behavioral Interventions: Impact of Treatment Fidelity Contemporary clinical trials","volume":"37","author":"Miller","year":"2014","journal-title":"Contemporary Clinical Trials"},{"issue":"6","key":"10.1016\/j.eswa.2026.132559_b0455","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/s41592-022-01488-1","article-title":"ColabFold: Making protein folding accessible to all","volume":"19","author":"Mirdita","year":"2022","journal-title":"Nature methods"},{"issue":"D1","key":"10.1016\/j.eswa.2026.132559_b0460","doi-asserted-by":"crossref","first-page":"D412","DOI":"10.1093\/nar\/gkaa913","article-title":"Pfam: The protein families database in 2021","volume":"49","author":"Mistry","year":"2021","journal-title":"Nucleic acids research"},{"issue":"47","key":"10.1016\/j.eswa.2026.132559_b0465","doi-asserted-by":"crossref","first-page":"57137","DOI":"10.1021\/acsomega.5c06443","article-title":"Multimodal Learning of Protein\u2013Protein Interactions for Accurate Binding Affinity Prediction","volume":"10","author":"Mo","year":"2025","journal-title":"ACS Omega"},{"key":"10.1016\/j.eswa.2026.132559_b0470","unstructured":"Mollon, M. F., Gonzalez-Rodriguez, J., Lozano-Diez, A., Ramos, D., & Toledano, D. T. (2025). Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark. arXiv preprint, arXiv:2501.18223. doi:10.48550\/arXiv.2501.18223."},{"key":"10.1016\/j.eswa.2026.132559_b0475","doi-asserted-by":"crossref","unstructured":"Mount, D. W. (2001). Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press. Cold Spring Harbor Laboratory Press. doi:10.1093\/bib\/5.4.393.","DOI":"10.1093\/bib\/5.4.393"},{"issue":"32","key":"10.1016\/j.eswa.2026.132559_b0480","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2303499120","article-title":"The transformative power of transformers in protein structure prediction","volume":"120","author":"Moussad","year":"2023","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.1016\/j.eswa.2026.132559_b0485","doi-asserted-by":"crossref","unstructured":"National Academies of Sciences, Medicine, Policy, Global Affairs, Committee on Science, Law, ... & Options for Future Management. (2017). Dual use research of concern in the life sciences: current issues and controversies. 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(2024_05). Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling. bioRxiv. doi:10.1101\/2024.05.31.596915.","DOI":"10.1101\/2024.05.31.596915"},{"issue":"3","key":"10.1016\/j.eswa.2026.132559_b0540","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nmeth.2340","article-title":"A large-scale evaluation of computational protein function prediction","volume":"10","author":"Radivojac","year":"2013","journal-title":"Nature Methods"},{"key":"10.1016\/j.eswa.2026.132559_b0545","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., . . . Ng, A. Y. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint, arXiv:1711.05225. doi:10.48550\/arXiv.1711.05225."},{"key":"10.1016\/j.eswa.2026.132559_b0550","doi-asserted-by":"crossref","unstructured":"Rao, R. M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., . . . Rives, A. (2021, July). MSA transformer. In International Conference on Machine Learning (pp. 8844-8856). PMLR. doi:10.1101\/2021.02.12.430858.","DOI":"10.1101\/2021.02.12.430858"},{"key":"10.1016\/j.eswa.2026.132559_b0555","doi-asserted-by":"crossref","unstructured":"Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2021). Transformer protein language models are unsupervised structure learners. bioRxiv. doi:10.1101\/2020.12.15.422761.","DOI":"10.1101\/2020.12.15.422761"},{"issue":"2","key":"10.1016\/j.eswa.2026.132559_b0560","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/nmeth.1818","article-title":"HHblits: Lightning-fast iterative protein sequence searching by HMM-HMM alignment","volume":"9","author":"Remmert","year":"2012","journal-title":"Nature methods"},{"key":"10.1016\/j.eswa.2026.132559_b0565","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1146\/annurev-biodatasci-080917-013508","article-title":"Large-Scale Analysis of Genetic and Clinical Patient Data","volume":"1","author":"Ritchie","year":"2018","journal-title":"Annual Review of Biomedical Data Science"},{"issue":"15","key":"10.1016\/j.eswa.2026.132559_b0570","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"5","key":"10.1016\/j.eswa.2026.132559_b0575","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132559_b0580","series-title":"Towards explainable artificial intelligence in biomedical applications. In Explainable AI: Interpreting, explaining and Visualizing Deep Learning Explainable AI: Interpreting, explaining and Visualizing Deep Learning","first-page":"5","author":"Samek","year":"2019"},{"key":"10.1016\/j.eswa.2026.132559_b0585","unstructured":"Sandbrink, J. B. (2023). Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools. arXiv, preprint arXiv:2306.13952. doi:10.48550\/arXiv.2306.13952."},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0590","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1038\/s41467-022-29268-7","article-title":"Current progress and open challenges for applying deep learning across the biosciences","volume":"13","author":"Sapoval","year":"2022","journal-title":"Nature Communications"},{"issue":"12","key":"10.1016\/j.eswa.2026.132559_b0595","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1145\/3381831","article-title":"Green AI","volume":"63","author":"Schwartz","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.eswa.2026.132559_b0600","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.coviro.2015.03.012","article-title":"Antibody specific epitope prediction\u2014emergence of a new paradigm","volume":"11","author":"Sela-Culang","year":"2015","journal-title":"Current Opinion in Virology"},{"issue":"13","key":"10.1016\/j.eswa.2026.132559_b0605","doi-asserted-by":"crossref","first-page":"i254","DOI":"10.1093\/bioinformatics\/bty275","article-title":"DeepFam: Deep learning based alignment-free method for protein family modeling and prediction","volume":"34","author":"Seo","year":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0610","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/pro.3290","article-title":"Clustal omega for making accurate alignments of many protein sequences","volume":"27","author":"Sievers","year":"2018","journal-title":"Protein Science"},{"key":"10.1016\/j.eswa.2026.132559_b0615","article-title":"InterPLM: Discovering interpretable features in protein language models via sparse autoencoders","volume":"1\u201311","author":"Simon","year":"2025","journal-title":"Nature Methods"},{"issue":"10","key":"10.1016\/j.eswa.2026.132559_b0620","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.cels.2021.08.010","article-title":"D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein\u2013protein interactions","volume":"12","author":"Sledzieski","year":"2021","journal-title":"Cell Systems"},{"key":"10.1016\/j.eswa.2026.132559_b0625","doi-asserted-by":"crossref","DOI":"10.2196\/44065","article-title":"Integrating Clinical Decision support into Electronic Health Record Systems using a Novel Platform (EvidencePoint): Developmental Study","volume":"7","author":"Solomon","year":"2023","journal-title":"JMIR formative research"},{"issue":"2","key":"10.1016\/j.eswa.2026.132559_b0630","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.jmb.2014.09.026","article-title":"The CamSol method of rational design of protein mutants with enhanced solubility","volume":"427","author":"Sormanni","year":"2015","journal-title":"Journal of molecular biology"},{"key":"10.1016\/j.eswa.2026.132559_b0635","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1109\/TCBB.2023.3237769","article-title":"BERT2OME: Prediction of 2-O-Methylation modifications from RNA Sequence by Transformer Architecture based on BERT","volume":"220","author":"Soylu","year":"2023","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132559_b0640","doi-asserted-by":"crossref","first-page":"810","DOI":"10.2174\/0115748936283134240109054157","article-title":"DeepPTM: Protein Post-translational Modification Prediction from Protein Sequences by Combining Deep Protein Language Model with Vision Transformers","volume":"19","author":"Soylu","year":"2024","journal-title":"Current Bioinformatics"},{"issue":"11","key":"10.1016\/j.eswa.2026.132559_b0645","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1038\/nbt.3988","article-title":"MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets","volume":"35","author":"Steinegger","year":"2017","journal-title":"Nature biotechnology"},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0650","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1186\/s12859-019-3019-7","article-title":"HH-suite3 for fast remote homology detection and deep protein annotation","volume":"20","author":"Steinegger","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0655","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: Benefits, risks, and strategies for success","volume":"3","author":"Sutton","year":"2020","journal-title":"NPJ digital medicine"},{"issue":"D1","key":"10.1016\/j.eswa.2026.132559_b0660","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: Protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Research"},{"issue":"11","key":"10.1016\/j.eswa.2026.132559_b0665","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0670","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nature medicine"},{"issue":"7873","key":"10.1016\/j.eswa.2026.132559_b0675","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1038\/s41586-021-03828-1","article-title":"Highly accurate protein structure prediction for the human proteome","volume":"596","author":"Tunyasuvunakool","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2026.132559_b0680","article-title":"The Age of AI in the Life Sciences: Benefits and Biosecurity Considerations","author":"Turner","year":"2025","journal-title":"National Academies of Sciences, Engineering, and Medicine."},{"issue":"3","key":"10.1016\/j.eswa.2026.132559_b0685","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1038\/s42256-022-00465-9","article-title":"Dual-use of artificial-intelligence-powered drug discovery","volume":"4","author":"Urbina","year":"2022","journal-title":"Nature machine intelligence"},{"issue":"7998","key":"10.1016\/j.eswa.2026.132559_b0690","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/s41586-023-06953-1","article-title":"De novo design of high-affinity binders of bioactive helical peptides","volume":"626","author":"V\u00e1zquez-Torres","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2026.132559_b0695","doi-asserted-by":"crossref","unstructured":"Vig, J., Madani, A., Varshney, R. L., Xiong, C., Socher, R., & Rajani, F. N. (2020). BERTology Meets Biology: Interpreting Attention in Protein Language Models. bioRxiv: The preprint server for biology. doi:10.1101\/2020.06.26.174417.","DOI":"10.1101\/2020.06.26.174417"},{"issue":"24","key":"10.1016\/j.eswa.2026.132559_b0700","doi-asserted-by":"crossref","first-page":"9135","DOI":"10.1021\/acs.jcim.4c00975","article-title":"Target-specific de novo peptide binder design with DiffPepBuilder","volume":"64","author":"Wang","year":"2024","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"7976","key":"10.1016\/j.eswa.2026.132559_b0705","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/s41586-023-06415-8","article-title":"De novo design of protein structure and function with RFdiffusion","volume":"620","author":"Watson","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2026.132559_b0710","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2025.1537471","article-title":"Responsible AI in biotechnology: Balancing discovery, innovation and biosecurity risks","volume":"13","author":"Wheeler","year":"2025","journal-title":"Frontiers in Bioengineering and Biotechnology"},{"issue":"6768","key":"10.1016\/j.eswa.2026.132559_b0715","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1126\/science.adu8578","article-title":"Strengthening nucleic acid biosecurity screening against generative protein design tools","volume":"390","author":"Wittmann","year":"2025","journal-title":"Science"},{"key":"10.1016\/j.eswa.2026.132559_b0720","doi-asserted-by":"crossref","unstructured":"Wu, R. D., Wang, R., Shen, R., Zhang, X., Luo, S. S., Wu, Z., . . . Peng, J. (2022). High-resolution de novo structure prediction from primary sequence. bioRxiv. doi:10.1101\/2022.07.21.500999.","DOI":"10.1101\/2022.07.21.500999"},{"issue":"8","key":"10.1016\/j.eswa.2026.132559_b0725","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1038\/s42256-023-00697-3","article-title":"Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning","volume":"5","author":"Yang","year":"2023","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"10.1016\/j.eswa.2026.132559_b0730","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/nmeth.3213","article-title":"The I-TASSER Suite: Protein structure and function prediction","volume":"12","author":"Yang","year":"2015","journal-title":"Nature methods"},{"issue":"3","key":"10.1016\/j.eswa.2026.132559_b0735","first-page":"482","article-title":"Sixty-five years of the long march in protein secondary structure prediction: The final stretch?","volume":"19","author":"Yang","year":"2018","journal-title":"Briefings in bioinformatics"},{"issue":"10","key":"10.1016\/j.eswa.2026.132559_b0740","doi-asserted-by":"crossref","first-page":"489","DOI":"10.3390\/toxins17100489","article-title":"ProToxin: A predictor of protein toxicity","volume":"17","author":"Yang","year":"2024","journal-title":"Toxins"},{"issue":"18","key":"10.1016\/j.eswa.2026.132559_b0745","doi-asserted-by":"crossref","first-page":"10798","DOI":"10.3390\/ijms231810798","article-title":"ProTstab2 for prediction of protein thermal stabilities","volume":"23","author":"Yang","year":"2022","journal-title":"International Journal of Molecular Sciences"},{"key":"10.1016\/j.eswa.2026.132559_b0750","first-page":"28","article-title":"Supervised Network-Based Fuzzy Learning of EEG Signals for Alzheimer\u2019s Disease Identification","author":"Yu","year":"2020","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.1016\/j.eswa.2026.132559_b0755","doi-asserted-by":"crossref","first-page":"4147","DOI":"10.1109\/JBHI.2025.3530922","article-title":"Neural Manifold Decoder for Acupuncture Stimulations with Representation Learning: An Acupuncture-Brain Interface","volume":"29","author":"Yu","year":"2025","journal-title":"IEEE Journal of Bioedical and Health Informatics"},{"issue":"7","key":"10.1016\/j.eswa.2026.132559_b0760","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1093\/bioinformatics\/btz863","article-title":"DeepMSA: Constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132559_b0765","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-9-40","article-title":"I-TASSER server for protein 3D structure prediction","volume":"9","author":"Zhang","year":"2008","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"10.1016\/j.eswa.2026.132559_b0770","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/s41592-023-02130-4","article-title":"Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data","volume":"21","author":"Zheng","year":"2024","journal-title":"Nature Methods"},{"key":"10.1016\/j.eswa.2026.132559_b0775","doi-asserted-by":"crossref","unstructured":"Zhu, J. L., Zheng, Z., Zhang, B., Zhong, B., Bai, J., Hong, X., . . . Chen, H. F. (2024). Precise generation of conformational ensembles for intrinsically disordered proteins via fine-tuned diffusion models. bioRxiv, 2024-05. doi:10.21203\/rs.3.rs-4489551\/v1.","DOI":"10.1101\/2024.05.05.592611"},{"issue":"12","key":"10.1016\/j.eswa.2026.132559_b0780","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010793","article-title":"Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction","volume":"18","author":"Zhu","year":"2022","journal-title":"PLOS Computational Biology"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426014727?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426014727?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:03:07Z","timestamp":1777572187000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426014727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":156,"alternative-id":["S0957417426014727"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132559","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From Heuristics to Deep Generative Models: A Critical Review of Protein Sequence Analysis Architectures for Clinical Decision Support Systems (CDSS)","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132559","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. 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