161 related articles for article (PubMed ID: 37722701)
1. Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning.
Li X; Yu X; Tian D; Liu Y; Li D
Med Phys; 2023 Nov; 50(11):7049-7059. PubMed ID: 37722701
[TBL] [Abstract][Full Text] [Related]
2. Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning.
Li D; Li X; Li S; Qi M; Sun X; Hu G
Heliyon; 2023 Mar; 9(3):e14374. PubMed ID: 36942252
[TBL] [Abstract][Full Text] [Related]
3. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.
Cao R; Yang F; Ma SC; Liu L; Zhao Y; Li Y; Wu DH; Wang T; Lu WJ; Cai WJ; Zhu HB; Guo XJ; Lu YW; Kuang JJ; Huan WJ; Tang WM; Huang K; Huang J; Yao J; Dong ZY
Theranostics; 2020; 10(24):11080-11091. PubMed ID: 33042271
[TBL] [Abstract][Full Text] [Related]
4. Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization.
Cui Z; Liang Z; Song B; Zhu Y; Chen G; Gu Y; Liang B; Ma J; Song B
Front Endocrinol (Lausanne); 2023; 14():1180732. PubMed ID: 37229449
[TBL] [Abstract][Full Text] [Related]
5. Identification and validation of an epithelial mesenchymal transition-related gene pairs signature for prediction of overall survival in patients with skin cutaneous melanoma.
Shi Y; Li Z; Zhou Z; Liao S; Wu Z; Li J; Yin J; Wang M; Weng M
PeerJ; 2022; 10():e12646. PubMed ID: 35116193
[TBL] [Abstract][Full Text] [Related]
6. Construction of mRNA prognosis signature associated with differentially expressed genes in early stage of stomach adenocarcinomas based on TCGA and GEO datasets.
Jiang F; Lin H; Yan H; Sun X; Yang J; Dong M
Eur J Med Res; 2022 Oct; 27(1):205. PubMed ID: 36253873
[TBL] [Abstract][Full Text] [Related]
7. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.
Wang R; Dai W; Gong J; Huang M; Hu T; Li H; Lin K; Tan C; Hu H; Tong T; Cai G
J Hematol Oncol; 2022 Jan; 15(1):11. PubMed ID: 35073937
[TBL] [Abstract][Full Text] [Related]
8. A disease network-based deep learning approach for characterizing melanoma.
Lai X; Zhou J; Wessely A; Heppt M; Maier A; Berking C; Vera J; Zhang L
Int J Cancer; 2022 Mar; 150(6):1029-1044. PubMed ID: 34716589
[TBL] [Abstract][Full Text] [Related]
9. A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.
Li W; Huang Q; Peng Y; Pan S; Hu M; Wang P; He Y
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15923-15938. PubMed ID: 37673824
[TBL] [Abstract][Full Text] [Related]
10. Transcriptomic Analysis Reveals Prognostic Molecular Signatures of Stage I Melanoma.
Thakur R; Laye JP; Lauss M; Diaz JMS; O'Shea SJ; Poźniak J; Filia A; Harland M; Gascoyne J; Randerson-Moor JA; Chan M; Mell T; Jönsson G; Bishop DT; Newton-Bishop J; Barrett JH; Nsengimana J
Clin Cancer Res; 2019 Dec; 25(24):7424-7435. PubMed ID: 31515461
[TBL] [Abstract][Full Text] [Related]
11. Prognostic score model-based signature genes for predicting the prognosis of metastatic skin cutaneous melanoma.
Wang J
Math Biosci Eng; 2021 Jun; 18(5):5125-5145. PubMed ID: 34517481
[TBL] [Abstract][Full Text] [Related]
12. Identification of Prognostic Biomarkers of Cutaneous Melanoma Based on Analysis of Tumor Mutation Burden.
Lin J; Lin Y; Huang Z; Li X
Comput Math Methods Med; 2020; 2020():8836493. PubMed ID: 33273963
[TBL] [Abstract][Full Text] [Related]
13. Development and validation of an immune gene set-based prognostic signature in cutaneous melanoma.
Tian Q; Gao H; Zhao W; Zhou Y; Yang J
Future Oncol; 2021 Nov; 17(31):4115-4129. PubMed ID: 34291650
[TBL] [Abstract][Full Text] [Related]
14. Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature.
Yang Y; Long X; Li G; Yu X; Liu Y; Li K; Tian X
Bioengineered; 2021 Dec; 12(1):1803-1812. PubMed ID: 34047683
[TBL] [Abstract][Full Text] [Related]
15. Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis.
Jia G; Song Z; Xu Z; Tao Y; Wu Y; Wan X
BMC Med Genomics; 2021 Apr; 14(1):96. PubMed ID: 33823876
[TBL] [Abstract][Full Text] [Related]
16. Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.
Korfiati A; Grafanaki K; Kyriakopoulos GC; Skeparnias I; Georgiou S; Sakellaropoulos G; Stathopoulos C
Int J Mol Sci; 2022 Jan; 23(3):. PubMed ID: 35163222
[TBL] [Abstract][Full Text] [Related]
17. Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients.
Peng Y; Chu Y; Chen Z; Zhou W; Wan S; Xiao Y; Zhang Y; Li J
World J Surg Oncol; 2020 Jun; 18(1):130. PubMed ID: 32546168
[TBL] [Abstract][Full Text] [Related]
18. Analyzing Prognostic Hub Genes in the Microenvironment of Cutaneous Melanoma by Computer Integrated Bioinformatics.
Li G; Zhang J; Liu Y; Cheng X; Sun K; Hong W; Sha K
Comput Intell Neurosci; 2022; 2022():4493347. PubMed ID: 35300397
[TBL] [Abstract][Full Text] [Related]
19. Comprehensive analysis of ferroptosis-related genes and prognosis of cutaneous melanoma.
Liu C; Liu Y; Yu Y; Zhao Y; Yu A
BMC Med Genomics; 2022 Mar; 15(1):39. PubMed ID: 35232428
[TBL] [Abstract][Full Text] [Related]
20. A novel RNA sequencing-based prognostic nomogram to predict survival for patients with cutaneous melanoma: Clinical trial/experimental study.
Tian J; Yang Y; Li MY; Zhang Y
Medicine (Baltimore); 2020 Jan; 99(3):e18868. PubMed ID: 32011509
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
