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Cited by in F6Publishing
For: Huang Z, Johnson TS, Han Z, Helm B, Cao S, Zhang C, Salama P, Rizkalla M, Yu CY, Cheng J, Xiang S, Zhan X, Zhang J, Huang K. Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations. BMC Med Genomics 2020;13:41. [PMID: 32241264 DOI: 10.1186/s12920-020-0686-1] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 7.5] [Reference Citation Analysis]
Number Citing Articles
1 Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F, Cereda M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int J Mol Sci 2021;22:4563. [PMID: 33925407 DOI: 10.3390/ijms22094563] [Reference Citation Analysis]
2 Zadeh Shirazi A, Fornaciari E, McDonnell MD, Yaghoobi M, Cevallos Y, Tello-Oquendo L, Inca D, Gomez GA. The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey. J Pers Med 2020;10:E224. [PMID: 33198332 DOI: 10.3390/jpm10040224] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
3 Oei RW, Lyu Y, Ye L, Kong F, Du C, Zhai R, Xu T, Shen C, He X, Kong L, Hu C, Ying H. Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics. J Pers Med 2021;11:787. [PMID: 34442430 DOI: 10.3390/jpm11080787] [Reference Citation Analysis]
4 Malenová G, Rowson D, Boeva V. Exploring Pathway-Based Group Lasso for Cancer Survival Analysis: A Special Case of Multi-Task Learning. Front Genet 2021;12:771301. [PMID: 34912376 DOI: 10.3389/fgene.2021.771301] [Reference Citation Analysis]
5 Yang X, Ding Y, Sun L, Shi M, Zhang P, He A, Zhang X, Huang Z, Li R. WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis. Front Oncol 2022;12:840038. [DOI: 10.3389/fonc.2022.840038] [Reference Citation Analysis]
6 Ebata K, Yamashiro S, Iida K, Okada M. Building patient-specific models for receptor tyrosine kinase signaling networks. FEBS J 2021. [PMID: 33755310 DOI: 10.1111/febs.15831] [Reference Citation Analysis]
7 Kuksin M, Morel D, Aglave M, Danlos FX, Marabelle A, Zinovyev A, Gautheret D, Verlingue L. Applications of single-cell and bulk RNA sequencing in onco-immunology. Eur J Cancer 2021;149:193-210. [PMID: 33866228 DOI: 10.1016/j.ejca.2021.03.005] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Torkey H, Atlam M, El-Fishawy N, Salem H. A novel deep autoencoder based survival analysis approach for microarray dataset. PeerJ Comput Sci 2021;7:e492. [PMID: 33981841 DOI: 10.7717/peerj-cs.492] [Reference Citation Analysis]
9 Ramirez R, Chiu YC, Zhang S, Ramirez J, Chen Y, Huang Y, Jin YF. Prediction and interpretation of cancer survival using graph convolution neural networks. Methods 2021;192:120-30. [PMID: 33484826 DOI: 10.1016/j.ymeth.2021.01.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
10 Meng X, Wang X, Zhang X, Zhang C, Zhang Z, Zhang K, Wang S. A Novel Attention-Mechanism Based Cox Survival Model by Exploiting Pan-Cancer Empirical Genomic Information. Cells 2022;11:1421. [DOI: 10.3390/cells11091421] [Reference Citation Analysis]
11 Phan NN, Chattopadhyay A, Lee TT, Yin HI, Lu TP, Lai LC, Hwa HL, Tsai MH, Chuang EY. High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data. Brief Bioinform 2021:bbab283. [PMID: 34368845 DOI: 10.1093/bib/bbab283] [Reference Citation Analysis]
12 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Reference Citation Analysis]
13 Zhang Z, Pan Q, Ge H, Xing L, Hong Y, Chen P. Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values. EBioMedicine 2020;62:103081. [PMID: 33181462 DOI: 10.1016/j.ebiom.2020.103081] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
14 Borisov N, Sergeeva A, Suntsova M, Raevskiy M, Gaifullin N, Mendeleeva L, Gudkov A, Nareiko M, Garazha A, Tkachev V, Li X, Sorokin M, Surin V, Buzdin A. Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles. Front Oncol 2021;11:652063. [PMID: 33937058 DOI: 10.3389/fonc.2021.652063] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 Zhang Z, Papa Akuetteh PD, Lin L, Wu Y, Li Y, Huang W, Ni H, Lv H, Zhang Q. Development and validation of a ferroptosis-related model for three digestive tract tumors based on a pan-cancer analysis. Epigenomics 2021;13:1497-514. [PMID: 34581636 DOI: 10.2217/epi-2021-0261] [Reference Citation Analysis]
16 Huang S, Huang Z, Chen P, Feng C. Aberrant Chloride Intracellular Channel 4 Expression Is Associated With Adverse Outcome in Cytogenetically Normal Acute Myeloid Leukemia. Front Oncol 2020;10:1648. [PMID: 33014825 DOI: 10.3389/fonc.2020.01648] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]