Scopus

Integrating Protein Language Models and Deep Learning for Immune Checkpoint Protein Prediction

Năm XB 2026 Tạp chí / Hội thảo Advances in Information and Communication Technology (ICTA 2025) Đơn vị ICTU DOI / Link https://doi.org/10.1007/978-3-032-18162-6_29 ↗

Tác giả

Tóm tắt

Immune checkpoint proteins (ICPs) are key regulators of immune responses and serve as crucial therapeutic targets in immuno-oncology. In this study, we present a computational framework for ICP identification based on a hybrid deep learning architecture that...

Tài liệu tham khảo

[1] Pardoll, D.M.: The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12(4), 252–264 (2012)

[2] Sharma, P., Allison, J.P.: The future of immune checkpoint therapy. Science 348(6230), 56–61 (2015)

[3] Cai, X., et al.: Current progress and future perspectives of immune checkpoint in cancer and infectious diseases. Front. Genet. 12, 785153 (2021)

[4] Wang, Y., et al.: Immune checkpoint modulators in cancer immunotherapy: recent advances and emerging concepts. J. Hematol. Oncol. 15(1), 111 (2022)

[5] Liu, Y., et al.: A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs. Front. Genet. 13, 1016449 (2022)

[6] Wang, Q., et al.: Immune checkpoint-related serum proteins and genetic variants predict outcomes of localized prostate cancer, a cohort study. Cancer Immunol. Immunother. 70(3), 701–712 (2021)

[7] Yoo, S.K., et al.: Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data. Nat. Med. 31(3), 869–880 (2025)

[8] Liu, Y., et al.: Predicting patient outcomes after treatment with immune checkpoint blockade: a review of biomarkers derived from diverse data modalities. Cell Genom. 4(1), 100444 (2024)

[9] Ibtehaz, N., et al.: Align-gram: rethinking the skip-gram model for protein sequence analysis. Protein J. 42(2), 135–146 (2023)

[10] Nguyen, V.-N., et al.: Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing. Methods 232, 65–71 (2024)

[11] Asgari, E., Mofrad, M.R.: Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10(11), e0141287 (2015)

[12] Lin, Z., et al.: Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637), 1123–1130 (2023)

[13] Rives, A., et al.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. U.S.A. 118(15), e2016239118 (2021)

[14] Di Gennaro, G., Buonanno, A., Palmieri, F.A.N.: Considerations about learning Word2Vec. J. Supercomput. 77(11), 12320–12335 (2021). https://doi.org/10.1007/s11227-021-03743-2

[15] Fu, L., et al.: CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23), 3150–3152 (2012)

[16] Israeli, S., Louzoun, Y.: Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections. Brief. Bioinform. 25(2), bbae084 (2024)

[17] Collatz, M., et al.: EpiDope: a deep neural network for linear B-cell epitope prediction. Bioinformatics 37(4), 448–455 (2021)

[18] Tng, S.S., et al.: Improved prediction model of protein lysine crotonylation sites using bidirectional recurrent neural networks. J. Proteome Res. 21(1), 265–273 (2022)

[19] Le Khanh, N.Q., et al.: Classification of adaptor proteins using recurrent neural networks and PSSM profiles. BMC Genom. 20(9), 966 (2019)

[20] Altschul, S.F., et al.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)

Ghi chú

Source ID: 312; Author classification: ICTU - Main Author; First listed author: Thi-Tuyen Nguyen; Contact author: Nui Nguyen, Nguyen Quoc Khanh Le; Total authors: 4; Not found in provided Springer books 10.1007/978-3-032-18159-6 or 10.1007/978-3-032-18316-3