BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

98 related articles for article (PubMed ID: 31946334)

  • 1. A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer
    Chen X; Zhou Z; Thomas K; Folkert M; Kim N; Rahimi A; Wang J
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():2182-2185. PubMed ID: 31946334
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model.
    Chen X; Zhou Z; Hannan R; Thomas K; Pedrosa I; Kapur P; Brugarolas J; Mou X; Wang J
    Phys Med Biol; 2018 Oct; 63(21):215008. PubMed ID: 30277889
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A multi-objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer.
    Wang K; Zhou Z; Wang R; Chen L; Zhang Q; Sher D; Wang J
    Med Phys; 2020 Oct; 47(10):5392-5400. PubMed ID: 32657426
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
    Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
    Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.
    Wang R; Weng Y; Zhou Z; Chen L; Hao H; Wang J
    Phys Med Biol; 2019 Dec; 64(24):245005. PubMed ID: 31698346
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.
    Klimov S; Miligy IM; Gertych A; Jiang Y; Toss MS; Rida P; Ellis IO; Green A; Krishnamurti U; Rakha EA; Aneja R
    Breast Cancer Res; 2019 Jul; 21(1):83. PubMed ID: 31358020
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.
    Asif M; Martiniano HFMCM; Vicente AM; Couto FM
    PLoS One; 2018; 13(12):e0208626. PubMed ID: 30532199
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Role of FDG-PET/CT in evaluating surgical outcomes of operable breast cancer--usefulness for malignant grade of triple-negative breast cancer.
    Ohara M; Shigematsu H; Tsutani Y; Emi A; Masumoto N; Ozaki S; Kadoya T; Okada M
    Breast; 2013 Oct; 22(5):958-63. PubMed ID: 23756383
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
    Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
    Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine learning for diagnostic ultrasound of triple-negative breast cancer.
    Wu T; Sultan LR; Tian J; Cary TW; Sehgal CM
    Breast Cancer Res Treat; 2019 Jan; 173(2):365-373. PubMed ID: 30343454
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning applications for the prediction of surgical site infection in neurological operations.
    Tunthanathip T; Sae-Heng S; Oearsakul T; Sakarunchai I; Kaewborisutsakul A; Taweesomboonyat C
    Neurosurg Focus; 2019 Aug; 47(2):E7. PubMed ID: 31370028
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Predicting post-stroke pneumonia using deep neural network approaches.
    Ge Y; Wang Q; Wang L; Wu H; Peng C; Wang J; Xu Y; Xiong G; Zhang Y; Yi Y
    Int J Med Inform; 2019 Dec; 132():103986. PubMed ID: 31629312
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.
    Nath A; Subbiah K
    Comput Biol Chem; 2015 Dec; 59 Pt A():101-10. PubMed ID: 26433483
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
    Pérez-Benito FJ; Signol F; Pérez-Cortés JC; Pollán M; Pérez-Gómez B; Salas-Trejo D; Casals M; Martínez I; LLobet R
    Comput Methods Programs Biomed; 2019 Aug; 177():123-132. PubMed ID: 31319940
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence.
    Witteveen A; Nane GF; Vliegen IMH; Siesling S; IJzerman MJ
    Med Decis Making; 2018 Oct; 38(7):822-833. PubMed ID: 30132386
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences.
    Ali S; Majid A
    J Biomed Inform; 2015 Apr; 54():256-69. PubMed ID: 25617669
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prognostic Value of a BCSC-associated MicroRNA Signature in Hormone Receptor-Positive HER2-Negative Breast Cancer.
    Gong C; Tan W; Chen K; You N; Zhu S; Liang G; Xie X; Li Q; Zeng Y; Ouyang N; Li Z; Zeng M; Zhuang S; Lau WY; Liu Q; Yin D; Wang X; Su F; Song E
    EBioMedicine; 2016 Sep; 11():199-209. PubMed ID: 27566954
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
    Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
    Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction.
    Turki T; Wei Z; Wang JTL
    J Bioinform Comput Biol; 2018 Jun; 16(3):1840014. PubMed ID: 29945499
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.
    Qian Z; Li Y; Wang Y; Li L; Li R; Wang K; Li S; Tang K; Zhang C; Fan X; Chen B; Li W
    Cancer Lett; 2019 Jun; 451():128-135. PubMed ID: 30878526
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.