157 related articles for article (PubMed ID: 38613345)
21. Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.
Xiao Z; Song Q; Wei Y; Fu Y; Huang D; Huang C
Transl Cancer Res; 2023 Dec; 12(12):3581-3590. PubMed ID: 38192980
[TBL] [Abstract][Full Text] [Related]
22. Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning.
Li D; Ding L; Luo J; Li QG
Front Immunol; 2023; 14():1192369. PubMed ID: 37304293
[TBL] [Abstract][Full Text] [Related]
23. Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis.
Adeoye J; Hui L; Koohi-Moghadam M; Tan JY; Choi SW; Thomson P
Int J Med Inform; 2022 Jan; 157():104635. PubMed ID: 34800847
[TBL] [Abstract][Full Text] [Related]
24. A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC.
Stüber AT; Coors S; Schachtner B; Weber T; Rügamer D; Bender A; Mittermeier A; Öcal O; Seidensticker M; Ricke J; Bischl B; Ingrisch M
Invest Radiol; 2023 Dec; 58(12):874-881. PubMed ID: 37504498
[TBL] [Abstract][Full Text] [Related]
25. Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer.
Ding H; Hu B; Guo R
PLoS One; 2024; 19(2):e0298125. PubMed ID: 38346070
[TBL] [Abstract][Full Text] [Related]
26.
Zhong H; Huang D; Wu J; Chen X; Chen Y; Huang C
BMC Med Imaging; 2023 Jun; 23(1):87. PubMed ID: 37370013
[TBL] [Abstract][Full Text] [Related]
27. Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With
Tang X; Li Y; Yan WF; Qian WL; Pang T; Gong YL; Yang ZG
Front Oncol; 2021; 11():719919. PubMed ID: 34660285
[TBL] [Abstract][Full Text] [Related]
28. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.
Yang X; Qiu H; Wang L; Wang X
J Med Internet Res; 2023 Oct; 25():e44417. PubMed ID: 37883174
[TBL] [Abstract][Full Text] [Related]
29. Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer.
Paik ES; Choi HJ; Kim TJ; Lee JW; Kim BG; Bae DS; Choi CH
Cancer Res Treat; 2018 Apr; 50(2):461-473. PubMed ID: 28546526
[TBL] [Abstract][Full Text] [Related]
30. Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study.
Leng Y; Kan A; Wang X; Li X; Xiao X; Wang Y; Liu L; Gong L
BMC Cancer; 2024 Mar; 24(1):307. PubMed ID: 38448945
[TBL] [Abstract][Full Text] [Related]
31. Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq.
Zhang D; Lu W; Cui S; Mei H; Wu X; Zhuo Z
J Ovarian Res; 2022 Nov; 15(1):123. PubMed ID: 36424614
[TBL] [Abstract][Full Text] [Related]
32. Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer.
Wang W; Li T; Xie Z; Zhao J; Zhang Y; Ruan Y; Han B
J Transl Med; 2024 Jan; 22(1):57. PubMed ID: 38221616
[TBL] [Abstract][Full Text] [Related]
33. Identification and validation of a seven m6A-related lncRNAs signature predicting prognosis of ovarian cancer.
Song Y; Qu H
BMC Cancer; 2022 Jun; 22(1):633. PubMed ID: 35676619
[TBL] [Abstract][Full Text] [Related]
34. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.
Geng Z; Yang C; Zhao Z; Yan Y; Guo T; Liu C; Wu A; Wu X; Wei L; Tian Y; Hu P; Wang K
J Transl Med; 2024 Mar; 22(1):236. PubMed ID: 38439097
[TBL] [Abstract][Full Text] [Related]
35. Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model.
Kim Y; Kim KH; Park J; Yoon HI; Sung W
Radiother Oncol; 2023 Jun; 183():109617. PubMed ID: 36921767
[TBL] [Abstract][Full Text] [Related]
36. Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer.
Zhang Y; Pei L
Aging (Albany NY); 2024 Feb; 16(4):3332-3349. PubMed ID: 38345575
[TBL] [Abstract][Full Text] [Related]
37. Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time.
Cygu S; Seow H; Dushoff J; Bolker BM
Sci Rep; 2023 Jan; 13(1):1370. PubMed ID: 36697455
[TBL] [Abstract][Full Text] [Related]
38. Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma.
Yuan D; Zhu H; Wang T; Zhang Y; Zheng X; Qu Y
Eur J Med Res; 2023 Oct; 28(1):465. PubMed ID: 37884970
[TBL] [Abstract][Full Text] [Related]
39. Four genes relevant to pathological grade and prognosis in ovarian cancer.
Pan X; Chen Y; Gao S
Cancer Biomark; 2020; 29(2):169-178. PubMed ID: 32444534
[TBL] [Abstract][Full Text] [Related]
40. A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs.
Zhao Q; Fan C
BMC Med Genet; 2019 Jun; 20(1):103. PubMed ID: 31182053
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
