BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

187 related articles for article (PubMed ID: 38612473)

  • 1. Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.
    Jaksik R; Szumała K; Dinh KN; Śmieja J
    Int J Mol Sci; 2024 Mar; 25(7):. PubMed ID: 38612473
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.
    Zhang S; Zeng X; Lin S; Liang M; Huang H
    J Clin Lab Anal; 2022 Feb; 36(2):e24190. PubMed ID: 34951053
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study.
    Li Y; Mansmann U; Du S; Hornung R
    Genes (Basel); 2021 Nov; 12(12):. PubMed ID: 34946821
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
    Liu C; Wang X; Genchev GZ; Lu H
    Methods; 2017 Jul; 124():100-107. PubMed ID: 28627406
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma.
    Xiong Y; Zhang Y; Liu N; Li Y; Liu H; Yang Q; Chen Y; Xia Z; Chen X; Wanggou S; Li X
    J Transl Med; 2023 Jul; 21(1):499. PubMed ID: 37491302
    [TBL] [Abstract][Full Text] [Related]  

  • 6. MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data.
    Dong Y; Yang W; Wang J; Zhao J; Qiang Y; Zhao Z; Kazihise NGF; Cui Y; Yang X; Liu S
    BMC Bioinformatics; 2019 Nov; 20(1):578. PubMed ID: 31726986
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.
    Ma H; Tong L; Zhang Q; Chang W; Li F
    Biomed Res Int; 2020; 2020():6427483. PubMed ID: 32596344
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
    Jalali-Najafabadi F; Stadler M; Dand N; Jadon D; Soomro M; Ho P; Marzo-Ortega H; Helliwell P; Korendowych E; Simpson MA; Packham J; Smith CH; Barker JN; McHugh N; Warren RB; Barton A; Bowes J; ;
    Sci Rep; 2021 Dec; 11(1):23335. PubMed ID: 34857774
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma.
    Zhao Y; Gao Y; Xu X; Zhou J; Wang H
    BMC Cancer; 2021 Mar; 21(1):257. PubMed ID: 33750346
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer.
    Bhadra T; Mallik S; Hasan N; Zhao Z
    BMC Bioinformatics; 2022 Apr; 23(Suppl 3):153. PubMed ID: 35484501
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data.
    El-Manzalawy Y; Hsieh TY; Shivakumar M; Kim D; Honavar V
    BMC Med Genomics; 2018 Sep; 11(Suppl 3):71. PubMed ID: 30255801
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis.
    Zhang Y; Xiong S; Wang Z; Liu Y; Luo H; Li B; Zou Q
    Methods; 2023 May; 213():1-9. PubMed ID: 36933628
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC.
    Guo S; Wang E; Wang B; Xue Y; Kuang Y; Liu H
    Ann Surg Oncol; 2024 Mar; 31(3):2078-2089. PubMed ID: 37996637
    [TBL] [Abstract][Full Text] [Related]  

  • 17. EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.
    Shao B; Bjaanæs MM; Helland Å; Schütte C; Conrad T
    PLoS One; 2019; 14(1):e0204186. PubMed ID: 30703089
    [TBL] [Abstract][Full Text] [Related]  

  • 18. LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.
    Yu J; Hu Y; Xu Y; Wang J; Kuang J; Zhang W; Shao J; Guo D; Wang Y
    BMC Cancer; 2019 Mar; 19(1):263. PubMed ID: 30902072
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma.
    Sun K; Zheng Y; Yang X; Jia W
    Med Biol Eng Comput; 2024 May; 62(5):1427-1440. PubMed ID: 38233683
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.
    Li Y; Guo Z; Gao X; Wang G
    Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38070154
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.