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Cited by in F6Publishing
For: Gong Y, Liao B, Peng D, Zou Q. Accurate Prediction and Key Feature Recognition of Immunoglobulin. Applied Sciences 2021;11:6894. [DOI: 10.3390/app11156894] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Ali Z, Alturise F, Alkhalifah T, Khan YD. IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach. Computational Intelligence and Neuroscience 2023;2023:1-13. [DOI: 10.1155/2023/2465414] [Reference Citation Analysis]
2 Bi XA, Mao Y, Luo S, Wu H, Zhang L, Luo X, Xu L. A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection. Brief Bioinform 2022;23:bbac454. [PMID: 36259367 DOI: 10.1093/bib/bbac454] [Reference Citation Analysis]
3 Wan H, Zhang J, Ding Y, Wang H, Tian G. Immunoglobulin Classification Based on FC* and GC* Features. Front Genet 2022;12:827161. [DOI: 10.3389/fgene.2021.827161] [Reference Citation Analysis]
4 Ghulam A, Sikander R, Ali F, Khan Swati ZN, Unar A, Talpur DB. Accurate prediction of immunoglobulin proteins using machine learning model. Informatics in Medicine Unlocked 2022;29:100885. [DOI: 10.1016/j.imu.2022.100885] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]