BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

582 related articles for article (PubMed ID: 32214348)

  • 1. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.
    López-García G; Jerez JM; Franco L; Veredas FJ
    PLoS One; 2020; 15(3):e0230536. PubMed ID: 32214348
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method.
    Xu Y; Cui X; Wang Y
    Front Cell Dev Biol; 2021; 9():675978. PubMed ID: 34179004
    [TBL] [Abstract][Full Text] [Related]  

  • 3. DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.
    Kakati T; Bhattacharyya DK; Kalita JK; Norden-Krichmar TM
    BMC Bioinformatics; 2022 Jan; 23(1):17. PubMed ID: 34991439
    [TBL] [Abstract][Full Text] [Related]  

  • 4. [Prognosis Prediction of Lung Cancer Patients Using CT Images: Feature Extraction by Convolutional Neural Network and Prediction by Machine Learning].
    Oshita Y; Takeuchi N; Teramoto A; Kondo M; Imaizumi K; Saito K; Fujita H
    Nihon Hoshasen Gijutsu Gakkai Zasshi; 2022 Aug; 78(8):829-837. PubMed ID: 35811128
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles.
    Matsubara T; Ochiai T; Hayashida M; Akutsu T; Nacher JC
    J Bioinform Comput Biol; 2019 Jun; 17(3):1940007. PubMed ID: 31288636
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Network-based drug sensitivity prediction.
    Ahmed KT; Park S; Jiang Q; Yeu Y; Hwang T; Zhang W
    BMC Med Genomics; 2020 Dec; 13(Suppl 11):193. PubMed ID: 33371891
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.
    Xiao Y; Wu J; Lin Z; Zhao X
    Comput Methods Programs Biomed; 2018 Nov; 166():99-105. PubMed ID: 30415723
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.
    Zhou X; Chai H; Zhao H; Luo CH; Yang Y
    Gigascience; 2020 Jul; 9(7):. PubMed ID: 32649756
    [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. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.
    Pan X; Shen HB
    BMC Bioinformatics; 2017 Feb; 18(1):136. PubMed ID: 28245811
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Recognition of peripheral blood cell images using convolutional neural networks.
    Acevedo A; Alférez S; Merino A; Puigví L; Rodellar J
    Comput Methods Programs Biomed; 2019 Oct; 180():105020. PubMed ID: 31425939
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.
    Xiao X; Wei G; Zhou L; Pan Y; Jing H; Zhao E; Yuan Y
    J Biomed Inform; 2021 Aug; 120():103840. PubMed ID: 34139331
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.
    Chereda H; Bleckmann A; Menck K; Perera-Bel J; Stegmaier P; Auer F; Kramer F; Leha A; Beißbarth T
    Genome Med; 2021 Mar; 13(1):42. PubMed ID: 33706810
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
    Chlis NK; Rausch L; Brocker T; Kranich J; Theis FJ
    Nucleic Acids Res; 2020 Nov; 48(20):11335-11346. PubMed ID: 33119742
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Bayesian variable selection with graphical structure learning: Applications in integrative genomics.
    Kundu S; Cheng Y; Shin M; Manyam G; Mallick BK; Baladandayuthapani V
    PLoS One; 2018; 13(7):e0195070. PubMed ID: 30059495
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.
    Chuang YH; Huang SH; Hung TM; Lin XY; Lee JY; Lai WS; Yang JM
    Sci Rep; 2021 Oct; 11(1):20691. PubMed ID: 34667236
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Skin lesion classification with ensembles of deep convolutional neural networks.
    Harangi B
    J Biomed Inform; 2018 Oct; 86():25-32. PubMed ID: 30103029
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Lung cancer survival period prediction and understanding: Deep learning approaches.
    Doppalapudi S; Qiu RG; Badr Y
    Int J Med Inform; 2021 Apr; 148():104371. PubMed ID: 33461009
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Improving Cancer Survival Prediction via Graph Convolutional Neural Network Learning on Protein-Protein Interaction Networks.
    Cai H; Liao Y; Zhu L; Wang Z; Song J
    IEEE J Biomed Health Inform; 2024 Feb; 28(2):1134-1143. PubMed ID: 37963003
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting enhancers with deep convolutional neural networks.
    Min X; Zeng W; Chen S; Chen N; Chen T; Jiang R
    BMC Bioinformatics; 2017 Dec; 18(Suppl 13):478. PubMed ID: 29219068
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 30.