Agrawal P, Bhagat D, Mahalwal M, Sharma N, Raghava GPS (2021) AntiCP 20: an updated model for predicting anticancer peptides. Briefings Bioinform 22(3):153. https://doi.org/10.1093/bib/bbaa153

Article  CAS  Google Scholar 

Ahmed S et al (2021) ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides. Sci Rep 11(1):23676. https://doi.org/10.1038/s41598-021-02703-3

Article  CAS  PubMed Central  Google Scholar 

Arif M, Musleh S, Fida H, Alam T (2024) PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation. Sci Rep 14:1. https://doi.org/10.1038/s41598-024-67433-8

Article  CAS  Google Scholar 

Bai, Shaojie, Kolter J, Koltun, Vladlen (2018) An mpirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR, abs/1803.01271. https://arxiv.org/abs/1803.01271

Bian J, Liu X, Dong G, Hou C, Huang S, Zhang D (2024) ACP-ML: a sequence-based method for anticancer peptide prediction. Comput Biol Med 170:108063. https://doi.org/10.1016/j.compbiomed.2024.108063

Article  CAS  Google Scholar 

Chen W, Ding H, Feng P, Lin H, Chou KC (2016) iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7(13):16895–16909. https://doi.org/10.18632/oncotarget.7815

Article  PubMed Central  Google Scholar 

Chen Z et al (2021) iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Research 49(10):e60. https://doi.org/10.1093/nar/gkab122

Article  CAS  PubMed Central  Google Scholar 

Chiangjong W, Chutipongtanate S, Hongeng S (2020) Anticancer peptide: physicochemical property, functional aspect and trend in clinical application (Review). Int J Oncol 57:678–696. https://doi.org/10.3892/ijo.2020.5099

Article  CAS  PubMed Central  Google Scholar 

Devlin J, Chang M.-W, Lee K, and Toutanova K (2019) "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in North American Chapter of the Association for Computational Linguistics. [Online]. Available: https://doi.org/10.48550/arXiv.1810.04805

Dong F, Zhao G, Tong H, Zhang Z, Lao X, Zheng H (2020) The prospect of bioactive peptide research: a review on databases and tools. Curr Bioinform 16:494–504. https://doi.org/10.2174/1574893615999200813192148

Article  CAS  Google Scholar 

Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23):3150–3152. https://doi.org/10.1093/bioinformatics/bts565

Article  CAS  PubMed Central  Google Scholar 

Gawde U et al (2023) CAMPR4: a database of natural and synthetic antimicrobial peptides. Nucleic Acids Res 51(D1):D377–D383. https://doi.org/10.1093/nar/gkac933

Article  CAS  Google Scholar 

Ge R, Feng G, Jing X, Zhang R, Wang P, Wu Q (2020) EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides. Front Genet 11:760. https://doi.org/10.3389/fgene.2020.00760

Article  CAS  PubMed Central  Google Scholar 

Guoxin Wang YL, Lei Cui, Tengchao Lv, Dinei Florencio, Cha Zhang, (2022) "A Simple yet Effective Learnable Positional Encoding Method for Improving Document Transformer Model," Association for Computational Linguistics, AACL-IJCNLP 2022, 453–463, 2022. https://doi.org/10.18653/v1/2022.findings-aacl.42

Hajisharifi Z, Piryaiee M, Mohammad Beigi M, Behbahani M, Mohabatkar H (2014) Predicting anticancer peptides with Chou′s pseudo amino acid composition and investigating their mutagenicity via Ames test. J Theor Biol 341:34–40. https://doi.org/10.1016/j.jtbi.2013.08.037

Article  CAS  Google Scholar 

He W, Wang Y, Cui L, Su R, Wei L, Martelli PL (2021) Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides. Bioinformatics 37(24):4684–4693. https://doi.org/10.1093/bioinformatics/btab560

Article  CAS  Google Scholar 

Hoskin DW, Ramamoorthy A (2008) Studies on anticancer activities of antimicrobial peptides. Biochimica et Biophysica Acta (BBA) - Biomembranes 1778(2):357–375. https://doi.org/10.1016/j.bbamem.2007.11.008

Article  CAS  Google Scholar 

Karim T, Shaon MSH, Sultan MF, Hasan MZ, Kafy AA (2024) "ANNprob-ACPs: a novel anticancer peptide identifier based on probabilistic feature fusion approach". Comput Biol Med 169 https://doi.org/10.1016/j.compbiomed.2023.107915

Kha QH, Le VH, Hung TN, Nguyen NT, Le NQ (2023) Development and validation of an explainable machine learning-based prediction model for drug-food interactions from chemical structures. Sensors 23(8):3962. https://doi.org/10.3390/s23083962

Article  CAS  PubMed Central  Google Scholar 

Kingma, Diederik & Ba, Jimmy (2014) Adam: a method for stochastic optimization, International Conference on Learning Representations. 1412(6980):6980. https://doi.org/10.48550/arXiv.1412.6980

Google Scholar 

Kumar Sangaraju V, Truong Pham N, Wei L, Yu X, Manavalan B (2024) mACPpred 20: stacked deep learning for anticancer peptide prediction with integrated spatial and probabilistic feature representations. J Mol Biol 436(17):168687. https://doi.org/10.1016/j.jmb.2024.168687

Article  CAS  Google Scholar 

Le NQK, Li W, Cao Y (2023) Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. Briefings Bioinform 24(5):bbad319. https://doi.org/10.1093/bib/bbad319

Article  Google Scholar 

Li F-M, Wang X-Q (2016) Identifying anticancer peptides by using improved hybrid compositions. Sci Rep 6(1):33910. https://doi.org/10.1038/srep33910

Article  CAS  PubMed Central  Google Scholar 

Li C, Zou Q, Jia C, Zheng J (2023) AMPpred-MFA: an interpretable antimicrobial peptide predictor with a stacking architecture, multiple features, and multihead attention. J Chem Inf Model 64(7):2393–2404. https://doi.org/10.1021/acs.jcim.3c01017

Article  CAS  Google Scholar 

Li Z et al (2023) ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides. Bioinformatics 39(3):btad108. https://doi.org/10.1093/bioinformatics/btad108

Article  CAS  PubMed Central  Google Scholar 

Liang X, Zhao H, Wang J (2024) MA-PEP: a novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism. Protein Sci 33(4):e4966. https://doi.org/10.1002/pro.4966

Article  CAS  PubMed Central  Google Scholar 

Liu Y, Yu Z, Chen C, Han Y, Yu B (2020) Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net. Anal Biochem 609:113903. https://doi.org/10.1016/j.ab.2020.113903

Article  CAS  Google Scholar 

Lundberg SM and Lee S.-I (2017) "A Unified Approach to Interpreting Model Predictions," in Neural Information Processing Systems. [Online]. Available: https://doi.org/10.48550/arXiv.1705.07874

LV Z, Cui F, Zou Q, Zhang L, Xu L (2021) Anticancer peptides prediction with deep representation learning features. Brief Bioinform 22(5):bbab008. https://doi.org/10.1093/bib/bbab008

Article  CAS  Google Scholar 

Ma K et al (2022) DC-CNN: Dual-channel convolutional neural networks with attention-pooling for fake news detection. Appl Intell 53(7):8354–8369. https://doi.org/10.1007/s10489-022-03910-9

Article  Google Scholar 

Ma T et al (2024) DRAMP 4.0: an open-access data repository dedicated to the clinical translation of antimicrobial peptides. Nucleic Acids Res 53:D403–D410. https://doi.org/10.1093/nar/gkae1046

Article  PubMed Central  Google Scholar 

Maaten LVD, Hinton GE (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579-2605. http://jmlr.org/papers/v9/vandermaaten08a.html

Google Scholar 

Maeda H, Khatami M (2018) Analyses of repeated failures in cancer therapy for solid tumors: poor tumor-selective drug delivery, low therapeutic efficacy and unsustainable costs. Clin Transl Med 7:1. https://doi.org/10.1186/s40169-018-0185-6

Article  Google Scholar 

Oord AVD et al (2016) "WaveNet: A Generative Model for Raw Audio," in Speech Synthesis Workshop

Rao B, Zhou C, Zhang G, Su R, Wei L (2020) ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides. Brief Bioinform 21(5):1846–1855. https://doi.org/10.1093/bib/bbz088

Article  Google Scholar 

Reshef DN et al (2011) Detecting Novel Associations in Large Data Sets. Science 334(6062):1518–1524. https://doi.org/10.1126/science.1205438

Article  CAS  PubMed Central  Google Scholar 

Soon NT, Chia YYA, Yap HW, Tang Y-Q (2020) Anticancer Mechanisms of Bioactive Peptides. Protein Pept Lett 27(9):823–830. https://doi.org/10.2174/0929866527666200409102747

Article  CAS  Google Scholar 

Sung H et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660