BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

153 related articles for article (PubMed ID: 38186568)

  • 1. Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach.
    Jia K; Wang Y; Cao QI; Wang Y
    Oncol Res; 2023; 32(2):409-419. PubMed ID: 38186568
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Molecular subtypes of lung adenocarcinoma patients for prognosis and therapeutic response prediction with machine learning on 13 programmed cell death patterns.
    Wei Q; Jiang X; Miao X; Zhang Y; Chen F; Zhang P
    J Cancer Res Clin Oncol; 2023 Oct; 149(13):11351-11368. PubMed ID: 37378675
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Investigating subtypes of lung adenocarcinoma by oxidative stress and immunotherapy related genes.
    Duan G; Huang C; Zhao J; Zhang Y; Zhao W; Dai H
    Sci Rep; 2023 Nov; 13(1):20930. PubMed ID: 38017020
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Pan-cancer analysis identifies proteasome 26S subunit, ATPase (PSMC) family genes, and related signatures associated with prognosis, immune profile, and therapeutic response in lung adenocarcinoma.
    Jia H; Tang WJ; Sun L; Wan C; Zhou Y; Shen WZ
    Front Genet; 2022; 13():1017866. PubMed ID: 36699466
    [No Abstract]   [Full Text] [Related]  

  • 5. Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis.
    Lin YT; Chu CY; Hung KS; Lu CH; Bednarczyk EM; Chen HY
    Comput Methods Programs Biomed; 2022 Oct; 225():107028. PubMed ID: 35930862
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A programmed cell death-related model based on machine learning for predicting prognosis and immunotherapy responses in patients with lung adenocarcinoma.
    Zhang Y; Wang Y; Chen J; Xia Y; Huang Y
    Front Immunol; 2023; 14():1183230. PubMed ID: 37671155
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.
    Zhao H; Su Y; Wang M; Lyu Z; Xu P; Jiao Y; Zhang L; Han W; Tian L; Fu P
    Front Oncol; 2022; 12():875761. PubMed ID: 35692759
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A prognosis-related molecular subtype for early-stage non-small lung cell carcinoma by multi-omics integration analysis.
    Yang K; Wu Y
    BMC Cancer; 2021 Feb; 21(1):128. PubMed ID: 33549049
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A nine-gene signature identification and prognostic risk prediction for patients with lung adenocarcinoma using novel machine learning approach.
    Dessie EY; Chang JG; Chang YS
    Comput Biol Med; 2022 Jun; 145():105493. PubMed ID: 35447457
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.
    Cai Q; He B; Zhang P; Zhao Z; Peng X; Zhang Y; Xie H; Wang X
    J Transl Med; 2020 Dec; 18(1):463. PubMed ID: 33287830
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Characterization of genomic instability-related genes predicts survival and therapeutic response in lung adenocarcinoma.
    Li S; Wang W; Yu H; Zhang S; Bi W; Sun S; Hong B; Fang Z; Chen X
    BMC Cancer; 2023 Nov; 23(1):1115. PubMed ID: 37974107
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.
    Zhang P; Pei S; Wu L; Xia Z; Wang Q; Huang X; Li Z; Xie J; Du M; Lin H
    Front Endocrinol (Lausanne); 2023; 14():1196372. PubMed ID: 37265698
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation.
    Wang Y; Xu Y; Liu C; Yuan C; Zhang Y
    Front Immunol; 2023; 14():1233260. PubMed ID: 37799714
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.
    Cui S; Luo Y; Tseng HH; Ten Haken RK; El Naqa I
    Med Phys; 2019 May; 46(5):2497-2511. PubMed ID: 30891794
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comprehensive Analysis and Reinforcement Learning of Hypoxic Genes Based on Four Machine Learning Algorithms for Estimating the Immune Landscape, Clinical Outcomes, and Therapeutic Implications in Patients With Lung Adenocarcinoma.
    Sun Z; Zeng Y; Yuan T; Chen X; Wang H; Ma X
    Front Immunol; 2022; 13():906889. PubMed ID: 35757722
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning based personalized drug response prediction for lung cancer patients.
    Qureshi R; Basit SA; Shamsi JA; Fan X; Nawaz M; Yan H; Alam T
    Sci Rep; 2022 Nov; 12(1):18935. PubMed ID: 36344580
    [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 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]  

  • 19. Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma.
    Yang D; He Y; Wu B; Liu R; Wang N; Wang T; Luo Y; Li Y; Liu Y
    Cancer Biomark; 2020; 29(3):399-416. PubMed ID: 32741804
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 8.