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For: Ge R, Feng G, Jing X, Zhang R, Wang P, Wu Q. EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides. Front Genet 2020;11:760. [PMID: 32903636 DOI: 10.3389/fgene.2020.00760] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhou C, Peng D, Liao B, Jia R, Wu F. ACP_MS: prediction of anticancer peptides based on feature extraction. Briefings in Bioinformatics 2022. [DOI: 10.1093/bib/bbac462] [Reference Citation Analysis]
2 Bhattarai S, Kim K, Tayara H, Chong KT. ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides. IJMS 2022;23:12194. [DOI: 10.3390/ijms232012194] [Reference Citation Analysis]
3 Akbar S, Hayat M, Tahir M, Khan S, Alarfaj FK. cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model. Artificial Intelligence in Medicine 2022;131:102349. [DOI: 10.1016/j.artmed.2022.102349] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Jia L, Luan Y. Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions. Interdiscip Sci 2022;14:545-54. [PMID: 35040094 DOI: 10.1007/s12539-022-00501-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Sun M, Yang S, Hu X, Zhou Y. ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information. Molecules 2022;27:1544. [PMID: 35268644 DOI: 10.3390/molecules27051544] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GP. In silico tools and databases for designing cancer immunotherapy. Immunotherapeutics 2022. [DOI: 10.1016/bs.apcsb.2021.11.008] [Reference Citation Analysis]
7 Cai L, Wang L, Fu X, Zeng X. Active Semisupervised Model for Improving the Identification of Anticancer Peptides. ACS Omega 2021;6:23998-4008. [PMID: 34568678 DOI: 10.1021/acsomega.1c03132] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Chen XG, Zhang W, Yang X, Li C, Chen H. ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation. Front Genet 2021;12:698477. [PMID: 34276801 DOI: 10.3389/fgene.2021.698477] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
10 Huang KY, Tseng YJ, Kao HJ, Chen CH, Yang HH, Weng SL. Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties. Sci Rep 2021;11:13594. [PMID: 34193950 DOI: 10.1038/s41598-021-93124-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
11 Lv Z, Cui F, Zou Q, Zhang L, Xu L. Anticancer peptides prediction with deep representation learning features. Brief Bioinform 2021:bbab008. [PMID: 33529337 DOI: 10.1093/bib/bbab008] [Cited by in Crossref: 30] [Cited by in F6Publishing: 33] [Article Influence: 30.0] [Reference Citation Analysis]
12 Ge R, Luo Y, Feng G, Jia G, Zhang H, Xu C, Xu G, Wang P. SuccSPred: Succinylation Sites Prediction Using Fused Feature Representation and Ranking Method. Bioinformatics Research and Applications 2021. [DOI: 10.1007/978-3-030-91415-8_17] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Liscano Y, Oñate-Garzón J, Delgado JP. Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides. Molecules 2020;25:E4245. [PMID: 32947811 DOI: 10.3390/molecules25184245] [Cited by in Crossref: 29] [Cited by in F6Publishing: 30] [Article Influence: 14.5] [Reference Citation Analysis]