Computational Molecular Biology 2025, Vol.15, No.3, 141-150 http://bioscipublisher.com/index.php/cmb 150 Kim S., Mollaei P., Antony A., Magar R., and Barati Farimani A., 2023, Gpcr-bert: interpreting sequential design of G protein-coupled receptors using protein language models, Journal of Chemical Information and Modeling, 64(4): 1134-1144. https://doi.org/10.1021/acs.jcim.3c01706 Luo F., Yang P., Li S., Ren X., and Sun X., 2020, CAPT: Contrastive pre-training for learning denoised sequence representations, arXiv Preprint, 2010: 6351. Matougui B., Belhadef H., and Kitouni I., 2020, An approach based on NLP for DNA sequence encoding using global vectors, In: International Conference of Reliable Information and Communication Technology, Springer International Publishing, pp.577-585. https://doi.org/10.1007/978-3-030-70713-2_53 Ng P., 2017, dna2vec: Consistent vector representations of variable-length k-mers, arXiv Preprint, 1701: 6279. https://doi.org/10.48550/arXiv.1701.06279 Pak M.A., Dovidchenko N., Sharma S.M., and Ivankov D., 2023, New mega dataset combined with deep neural network makes a progress in predicting impact of mutation on protein stability, bioRxiv, 31: 522396. https://doi.org/10.1101/2022.12.31.522396 Pan X., and Shen H., 2018, Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites, Neurocomputing, 305: 51-58. https://doi.org/10.1016/j.neucom.2018.04.036 Ramprasath M., Dhanasekaran K., Karthick T., Velumani R., and Sudhakaran P., 2022, An extensive study on pretrained models for natural language processing based on transformers, In: 2022 International Conference on Electronics and Renewable Systems (ICEARS), IEEE, pp.382-389. https://doi.org/10.1109/ICEARS53579.2022.9752241 Ruan W., Lyu Y., Zhang J., Cai J., Shu P., Ge Y., Lu Y., Gao S., Wang Y., Wang P., Zhao L., Wang T., Liu Y., Fang L., Liu Z., Li Y., Wu Z., Chen J., Jiang H., Pan Y., Yang Z., Chen J., Liang S., Zhang W., Ma T., Dou Y., Zhang J., Gong X., Gan Q., Zou Y.X., Chen Z.C., Qian Y., Yu S.R., Lu J., Song K., Wang X., Sikora A., Li G., Li X., Wang Y., Zhang L., Abate Y., He L., Zhong W., Liu R., Huang C., Liu W., Shen Y., Ma P., Zhu H., Yan Y., Zhu D., and Liu T., 2025, Large language models for bioinformatics, Quantitative Biology, 14(1): e70014. https://doi.org/10.1002/qub2.70014 Sanabria M., Hirsch J., Joubert P.M., and Poetsch A., 2024, DNA language model GROVER learns sequence context in the human genome, Nature Machine Intelligence, 6(8): 911-923. https://doi.org/10.1038/s42256-024-00872-0 Shahid U., 2023, Leveraging fine-tuned large language models in bioinformatics: a research perspective, Qeios, 10: 32388. https://doi.org/10.32388/WE7UMN.2 Song B., Li Z., Lin X., Wang J., Wang T., and Fu X.Z., 2021, Pretraining model for biological sequence data, Briefings in Functional Genomics, 20(3): 181-195. https://doi.org/10.1093/bfgp/elab025 Sun H., and Shen B., 2025, Structure-informed protein language models are robust to missense variants, Human Genetics, 144(2): 209-225. https://doi.org/10.21203/rs.3.rs-3219092/v1 Uribe D., Cuan E., and Urquizo E., 2022, Fine-tuning of BERT models for sequence classification, In: 2022 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE), IEEE, pp.140-144. https://doi.org/10.1109/ICMEAE58636.2022.00031 Wang N., Bian J., Li Y., Li X., Mumtaz S., Kong L., and Xiong H., 2024, Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning, Nature Machine Intelligence, 6(5): 548-557. https://doi.org/10.1038/s42256-024-00836-4 Wang Z., Wang Z., Jiang J., Chen P., Shi X., and Li Y., 2025, Large language models in bioinformatics: a survey, arXiv Preprint, 2025(v1): 4490. https://doi.org/10.18653/v1/2025.findings-acl.184 Yang W., Liu C., and Li Z., 2023, Lightweight fine-tuning a pretrained protein language model for protein secondary structure prediction, bioRxiv, 22: 530066. https://doi.org/10.1101/2023.03.22.530066 Zhang Y., Gao Z., Tan C., and Li S.Z., 2023, Efficiently predicting protein stability changes upon single-point mutation with large language models, arXiv Preprint, 2312: 4019. https://doi.org/10.48550/arXiv.2312.04019
RkJQdWJsaXNoZXIy MjQ4ODYzNA==