163 related articles for article (PubMed ID: 35615023)
1. Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma.
He T; Li J; Wang P; Zhang Z
Comput Struct Biotechnol J; 2022; 20():2352-2359. PubMed ID: 35615023
[TBL] [Abstract][Full Text] [Related]
2. Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma.
Liang J; He T; Li H; Guo X; Zhang Z
J Transl Med; 2022 Jun; 20(1):293. PubMed ID: 35765031
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms.
Zhang Z; Li J; He T; Ding J
Front Oncol; 2020; 10():330. PubMed ID: 32296631
[TBL] [Abstract][Full Text] [Related]
5. Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools.
Lin J; Lu S; Jiang Z; Hu C; Zhang Z
PeerJ; 2021; 9():e11412. PubMed ID: 34012732
[TBL] [Abstract][Full Text] [Related]
6. Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system.
Zhang Z; He T; Huang L; Li J; Wang P
Comput Struct Biotechnol J; 2021; 19():2329-2346. PubMed ID: 34025929
[TBL] [Abstract][Full Text] [Related]
7. Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.
Zhang Z; Huang L; Li J; Wang P
BMC Bioinformatics; 2022 Apr; 23(1):124. PubMed ID: 35395711
[TBL] [Abstract][Full Text] [Related]
8. [Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the
Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules].
Su Z; Mao W; Li B; Zheng Z; Yang B; Ren M; Song T; Feng H; Meng Y
Zhongguo Fei Ai Za Zhi; 2022 Apr; 25(4):245-252. PubMed ID: 35477188
[TBL] [Abstract][Full Text] [Related]
9. A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma.
Wang X; Zhou B; Xia Y; Zuo J; Liu Y; Bi X; Luo X; Zhang C
BMC Cancer; 2021 Jul; 21(1):801. PubMed ID: 34247575
[TBL] [Abstract][Full Text] [Related]
10. Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages.
He T; Li H; Zhang Z
J Ovarian Res; 2023 May; 16(1):92. PubMed ID: 37170143
[TBL] [Abstract][Full Text] [Related]
11. An
Han Y; Wong FC; Wang D; Kahlert C
Cancer Inform; 2022; 21():11769351221100727. PubMed ID: 35645555
[TBL] [Abstract][Full Text] [Related]
12. Comprehensive bioinformatics analysis reveals potential lncRNA biomarkers for overall survival in patients with hepatocellular carcinoma: an on-line individual risk calculator based on TCGA cohort.
Zhang Z; Ouyang Y; Huang Y; Wang P; Li J; He T; Liu Q
Cancer Cell Int; 2019; 19():174. PubMed ID: 31312112
[TBL] [Abstract][Full Text] [Related]
13. Development and Assessment of a Clinical Calculator for Estimating the Likelihood of Recurrence and Survival Among Patients With Locally Advanced Rectal Cancer Treated With Chemotherapy, Radiotherapy, and Surgery.
Weiser MR; Chou JF; Keshinro A; Chapman WC; Bauer PS; Mutch MG; Parikh PJ; Cercek A; Saltz LB; Gollub MJ; Romesser PB; Crane CH; Shia J; Markowitz AJ; Garcia-Aguilar J; Gönen M;
JAMA Netw Open; 2021 Nov; 4(11):e2133457. PubMed ID: 34748003
[TBL] [Abstract][Full Text] [Related]
14. Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics.
Li H; Liu SB; Shen J; Bai L; Zhang X; Cao J; Yi N; Lu K; Tang Z
Front Oncol; 2022; 12():895148. PubMed ID: 35785155
[TBL] [Abstract][Full Text] [Related]
15. A Recurrence-Specific Gene-Based Prognosis Prediction Model for Lung Adenocarcinoma through Machine Learning Algorithm.
Xu S; Zhou J; Liu K; Chen Z; He Z
Biomed Res Int; 2020; 2020():9124792. PubMed ID: 33224985
[TBL] [Abstract][Full Text] [Related]
16. Identifying
Li J; Liu Y; Dong W; Zhou Y; Wu J; Luan K; Qi L
Quant Imaging Med Surg; 2022 Mar; 12(3):1893-1908. PubMed ID: 35284267
[TBL] [Abstract][Full Text] [Related]
17. A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.
Li Y; Ge D; Gu J; Xu F; Zhu Q; Lu C
BMC Cancer; 2019 Sep; 19(1):886. PubMed ID: 31488089
[TBL] [Abstract][Full Text] [Related]
18. A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD).
Al-Dherasi A; Huang QT; Liao Y; Al-Mosaib S; Hua R; Wang Y; Yu Y; Zhang Y; Zhang X; Huang C; Mousa H; Ge D; Sufiyan S; Bai W; Liu R; Shao Y; Li Y; Zhang J; Shi L; Lv D; Li Z; Liu Q
Cancer Cell Int; 2021 Jun; 21(1):294. PubMed ID: 34092242
[TBL] [Abstract][Full Text] [Related]
19. Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model.
Zhang ZQ; He G; Luo ZW; Cheng CC; Wang P; Li J; Zhu MG; Ming L; He TS; Ouyang YL; Huang YY; Wu XL; Ye YN
Chin Med J (Engl); 2021 Jun; 134(14):1701-1708. PubMed ID: 34133353
[TBL] [Abstract][Full Text] [Related]
20. Two predictive precision medicine tools for hepatocellular carcinoma.
Zhang Z; Li J; He T; Ouyang Y; Huang Y; Liu Q; Wang P; Ding J
Cancer Cell Int; 2019; 19():290. PubMed ID: 31754347
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
