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For: Jiao S, Zou Q, Guo H, Shi L. iTTCA-RF: a random forest predictor for tumor T cell antigens. J Transl Med 2021;19:449. [PMID: 34706730 DOI: 10.1186/s12967-021-03084-x] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Charoenkwan P, Pipattanaboon C, Nantasenamat C, Hasan MM, Moni MA, Lio' P, Shoombuatong W. PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Comput Biol Med 2023;152:106368. [PMID: 36481763 DOI: 10.1016/j.compbiomed.2022.106368] [Reference Citation Analysis]
2 Herrera-bravo J, Farías JG, Sandoval C, Herrera-belén L, Quiñones J, Díaz R, Beltrán JF. nAChR-PEP-PRED: A Robust Tool for Predicting Peptide Inhibitors of Acetylcholine Receptors Using the Random Forest Classifier. Int J Pept Res Ther 2022;28. [DOI: 10.1007/s10989-022-10460-8] [Reference Citation Analysis]
3 Hu R, Wu J, Zhang L, Zhou X, Zhang Y. CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach. Front Genet 2022;13:935989. [DOI: 10.3389/fgene.2022.935989] [Reference Citation Analysis]
4 Zhao S, Ding Y, Liu X, Su X. HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins. Comput Biol Med 2022;145:105395. [PMID: 35334314 DOI: 10.1016/j.compbiomed.2022.105395] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Lin C, Wang L, Shi L. AAPred-CNN: accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides. Methods 2022:S1046-2023(22)00010-X. [PMID: 35031486 DOI: 10.1016/j.ymeth.2022.01.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
6 Niu M, Zou Q, Lin C. CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach. PLoS Comput Biol 2022;18:e1009798. [PMID: 35051187 DOI: 10.1371/journal.pcbi.1009798] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
7 Han Y, Yang H, Huang Q, Sun Z, Li M, Zhang J, Deng K, Chen S, Lin H; Beijing Physical Examination Center, Beijing, China, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China. . MBE 2022;19:3597-608. [DOI: 10.3934/mbe.2022166] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
8 Cui F, Li S, Zhang Z, Sui M, Cao C, El-latif Hesham A, Zou Q. DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins. Computational and Structural Biotechnology Journal 2022;20:2020-8. [DOI: 10.1016/j.csbj.2022.04.029] [Reference Citation Analysis]
9 Vlachas C, Damianos L, Gousetis N, Mouratidis I, Kelepouris D, Kollias K, Asimopoulos N, Fragulis GF, Roy D, Fragulis G. Random forest classification algorithm for medical industry data. SHS Web Conf 2022;139:03008. [DOI: 10.1051/shsconf/202213903008] [Reference Citation Analysis]
10 Gu X, Guo L, Liao B, Jiang Q. Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D. Front Genet 2021;12:796327. [PMID: 34925468 DOI: 10.3389/fgene.2021.796327] [Reference Citation Analysis]
11 Guo Y, Hou L, Zhu W, Wang P. Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes. Front Genet 2021;12:797641. [PMID: 34887905 DOI: 10.3389/fgene.2021.797641] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]