BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

154 related articles for article (PubMed ID: 34727887)

  • 1. PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.
    Bektaş AB; Gönen M
    BMC Bioinformatics; 2021 Nov; 22(1):537. PubMed ID: 34727887
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Discriminating early- and late-stage cancers using multiple kernel learning on gene sets.
    Rahimi A; Gönen M
    Bioinformatics; 2018 Jul; 34(13):i412-i421. PubMed ID: 29949993
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning.
    Dereli O; Oğuz C; Gönen M
    Bioinformatics; 2019 Dec; 35(24):5137-5145. PubMed ID: 31147687
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers.
    Rahimi A; Gönen M
    Bioinformatics; 2020 Jun; 36(12):3766-3772. PubMed ID: 32163111
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.
    Mendez KM; Reinke SN; Broadhurst DI
    Metabolomics; 2019 Nov; 15(12):150. PubMed ID: 31728648
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.
    Zhang X; Jang MI; Zheng Z; Gao A; Lin Z; Kim KY
    Anticancer Res; 2021 May; 41(5):2419-2429. PubMed ID: 33952467
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Fast and interpretable genomic data analysis using multiple approximate kernel learning.
    Bektaş AB; Ak Ç; Gönen M
    Bioinformatics; 2022 Jun; 38(Suppl 1):i77-i83. PubMed ID: 35758810
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation.
    Gönen M
    BMC Bioinformatics; 2016 Dec; 17(Suppl 16):0. PubMed ID: 28105911
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study.
    Kweon S; Lee JH; Lee Y; Park YR
    J Med Internet Res; 2020 Aug; 22(8):e18387. PubMed ID: 32773372
    [TBL] [Abstract][Full Text] [Related]  

  • 10. CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features.
    Zhang X; Wang J; Li J; Chen W; Liu C
    BMC Med Genomics; 2018 Dec; 11(Suppl 6):120. PubMed ID: 30598114
    [TBL] [Abstract][Full Text] [Related]  

  • 11. PIMKL: Pathway-Induced Multiple Kernel Learning.
    Manica M; Cadow J; Mathis R; Rodríguez Martínez M
    NPJ Syst Biol Appl; 2019; 5():8. PubMed ID: 30854223
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus.
    Haga H; Sato H; Koseki A; Saito T; Okumoto K; Hoshikawa K; Katsumi T; Mizuno K; Nishina T; Ueno Y
    PLoS One; 2020; 15(11):e0242028. PubMed ID: 33152046
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Application of ensemble learning to genomic selection in chinese simmental beef cattle.
    Liang M; Miao J; Wang X; Chang T; An B; Duan X; Xu L; Gao X; Zhang L; Li J; Gao H
    J Anim Breed Genet; 2021 May; 138(3):291-299. PubMed ID: 33089920
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis.
    Yperman J; Becker T; Valkenborg D; Popescu V; Hellings N; Wijmeersch BV; Peeters LM
    BMC Neurol; 2020 Mar; 20(1):105. PubMed ID: 32199461
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
    Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
    Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers.
    Kawakami E; Tabata J; Yanaihara N; Ishikawa T; Koseki K; Iida Y; Saito M; Komazaki H; Shapiro JS; Goto C; Akiyama Y; Saito R; Saito M; Takano H; Yamada K; Okamoto A
    Clin Cancer Res; 2019 May; 25(10):3006-3015. PubMed ID: 30979733
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma.
    Li MX; Sun XM; Cheng WG; Ruan HJ; Liu K; Chen P; Xu HJ; Gao SG; Feng XS; Qi YJ
    BMC Cancer; 2021 Aug; 21(1):906. PubMed ID: 34372798
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
    Su M; Feng G; Liu Z; Li Y; Wang R
    J Chem Inf Model; 2020 Mar; 60(3):1122-1136. PubMed ID: 32085675
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.