157 related articles for article (PubMed ID: 38613345)
1. Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer.
Sun W; Xu P; Gao K; Lian W; Sun X
J Cell Mol Med; 2024 Apr; 28(8):e18309. PubMed ID: 38613345
[TBL] [Abstract][Full Text] [Related]
2. Clustering and machine learning-based integration identify cancer associated fibroblasts genes' signature in head and neck squamous cell carcinoma.
Wang Q; Zhao Y; Wang F; Tan G
Front Genet; 2023; 14():1111816. PubMed ID: 37065499
[No Abstract] [Full Text] [Related]
3. Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome.
Hu X; Dong H; Qin W; Bin Y; Huang W; Kang M; Wang R
Front Pharmacol; 2024; 15():1341346. PubMed ID: 38666027
[TBL] [Abstract][Full Text] [Related]
4. Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.
Xu QT; Qiang JK; Huang ZY; Jiang WJ; Cui XM; Hu RH; Wang T; Yi XL; Li JY; Yu Z; Zhang S; Du T; Liu J; Jiang XH
Environ Toxicol; 2024 May; 39(5):2908-2926. PubMed ID: 38299230
[TBL] [Abstract][Full Text] [Related]
5. Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer.
Wang L; Liu Z; Liang R; Wang W; Zhu R; Li J; Xing Z; Weng S; Han X; Sun YL
Elife; 2022 Oct; 11():. PubMed ID: 36282174
[TBL] [Abstract][Full Text] [Related]
6. An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.
Cui Y; Zhang W; Lu W; Feng Y; Wu X; Zhuo Z; Zhang D; Zhang Y
Front Immunol; 2024; 15():1228235. PubMed ID: 38404588
[TBL] [Abstract][Full Text] [Related]
7. The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study.
Xiao J; Mo M; Wang Z; Zhou C; Shen J; Yuan J; He Y; Zheng Y
JMIR Med Inform; 2022 Feb; 10(2):e33440. PubMed ID: 35179504
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.
Wang D; Pan B; Huang JC; Chen Q; Cui SP; Lang R; Lyu SC
Front Oncol; 2023; 13():1106029. PubMed ID: 37007095
[TBL] [Abstract][Full Text] [Related]
9. Cancer survival classification using integrated data sets and intermediate information.
Kim S; Park T; Kon M
Artif Intell Med; 2014 Sep; 62(1):23-31. PubMed ID: 24997860
[TBL] [Abstract][Full Text] [Related]
10. Machine learning developed a PI3K/Akt pathway-related signature for predicting prognosis and drug sensitivity in ovarian cancer.
Han X; Yang L; Tian H; Ji Y
Aging (Albany NY); 2023 Oct; 15(20):11162-11183. PubMed ID: 37851341
[TBL] [Abstract][Full Text] [Related]
11. Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database.
Sun H; Wu S; Li S; Jiang X
Medicine (Baltimore); 2023 Mar; 102(10):e33144. PubMed ID: 36897699
[TBL] [Abstract][Full Text] [Related]
12. A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database.
Fan W; Chen X; Li R; Zheng R; Wang Y; Guo Y
Biochem Genet; 2023 Feb; 61(1):138-150. PubMed ID: 35761155
[TBL] [Abstract][Full Text] [Related]
13. Ovarian cancer classification and prognosis assessment model based on prognostic target genes in key microRNA-target gene networks.
Chen X; Li Y; He J
J Gene Med; 2024 Jan; 26(1):e3575. PubMed ID: 37548130
[TBL] [Abstract][Full Text] [Related]
14. Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer.
Huan Q; Cheng S; Ma HF; Zhao M; Chen Y; Yuan X
J Cell Mol Med; 2024 Jan; 28(1):e18021. PubMed ID: 37994489
[TBL] [Abstract][Full Text] [Related]
15. Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method.
Ma B; Geng Y; Meng F; Yan G; Song F
J Cancer; 2020; 11(5):1288-1298. PubMed ID: 31956375
[No Abstract] [Full Text] [Related]
16. Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System.
He T; Huang L; Li J; Wang P; Zhang Z
Front Med (Lausanne); 2021; 8():587496. PubMed ID: 34109184
[No Abstract] [Full Text] [Related]
17. A novel extrachromosomal circular DNA related genes signature for overall survival prediction in patients with ovarian cancer.
Zhang Y; Dong K; Jia X; Du S; Wang D; Wang L; Qu H; Zhu S; Wang Y; Wang Z; Zhang S; Sun W; Fu S
BMC Med Genomics; 2023 Jun; 16(1):140. PubMed ID: 37337170
[TBL] [Abstract][Full Text] [Related]
18. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population.
Chowdhury MZI; Leung AA; Walker RL; Sikdar KC; O'Beirne M; Quan H; Turin TC
Sci Rep; 2023 Jan; 13(1):13. PubMed ID: 36593280
[TBL] [Abstract][Full Text] [Related]
19. A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer.
Peng Y; Wang H; Huang Q; Wu J; Zhang M
J Ovarian Res; 2022 Jan; 15(1):8. PubMed ID: 35031063
[TBL] [Abstract][Full Text] [Related]
20. Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies.
Wu Q; Tian R; He X; Liu J; Ou C; Li Y; Fu X
Front Immunol; 2023; 14():1164408. PubMed ID: 37090728
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]