BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

519 related articles for article (PubMed ID: 33737557)

  • 1. Integrated multi-omics analysis of ovarian cancer using variational autoencoders.
    Hira MT; Razzaque MA; Angione C; Scrivens J; Sawan S; Sarker M
    Sci Rep; 2021 Mar; 11(1):6265. PubMed ID: 33737557
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
    Wu D; Wang D; Zhang MQ; Gu J
    BMC Genomics; 2015 Dec; 16():1022. PubMed ID: 26626453
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.
    Withnell E; Zhang X; Sun K; Guo Y
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34402865
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.
    Chung NC; Mirza B; Choi H; Wang J; Wang D; Ping P; Wang W
    Methods; 2019 Aug; 166():66-73. PubMed ID: 30853547
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.
    Tong L; Wu H; Wang MD
    Methods; 2021 May; 189():74-85. PubMed ID: 32763377
    [TBL] [Abstract][Full Text] [Related]  

  • 8. PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.
    Lemsara A; Ouadfel S; Fröhlich H
    BMC Bioinformatics; 2020 Apr; 21(1):146. PubMed ID: 32299344
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders.
    Way GP; Greene CS
    Pac Symp Biocomput; 2018; 23():80-91. PubMed ID: 29218871
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches.
    Lee D; Park Y; Kim S
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 34020548
    [TBL] [Abstract][Full Text] [Related]  

  • 11. MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.
    Rong Z; Liu Z; Song J; Cao L; Yu Y; Qiu M; Hou Y
    Comput Biol Med; 2022 Nov; 150():106085. PubMed ID: 36162197
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Decoding regulatory structures and features from epigenomics profiles: A Roadmap-ENCODE Variational Auto-Encoder (RE-VAE) model.
    Hu R; Pei G; Jia P; Zhao Z
    Methods; 2021 May; 189():44-53. PubMed ID: 31672653
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.
    Chung RH; Kang CY
    Gigascience; 2019 May; 8(5):. PubMed ID: 31029063
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders.
    Wang Z; Wang Y
    BMC Bioinformatics; 2019 Nov; 20(Suppl 18):568. PubMed ID: 31760935
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.
    Chaudhary K; Poirion OB; Lu L; Garmire LX
    Clin Cancer Res; 2018 Mar; 24(6):1248-1259. PubMed ID: 28982688
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
    Kim D; Li R; Dudek SM; Ritchie MD
    J Biomed Inform; 2015 Aug; 56():220-8. PubMed ID: 26048077
    [TBL] [Abstract][Full Text] [Related]  

  • 17. MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine.
    Xie B; Yuan Z; Yang Y; Sun Z; Zhou S; Fang X
    Breast Cancer Res Treat; 2018 Jun; 169(3):625-632. PubMed ID: 29429018
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping.
    Madhumita ; Paul S
    Comput Biol Med; 2022 Sep; 148():105832. PubMed ID: 35834966
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder.
    Wang HQ; Li HL; Han JL; Feng ZP; Deng HX; Han X
    Comput Biol Chem; 2023 Aug; 105():107906. PubMed ID: 37336028
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network.
    Wang H; Han X; Ren J; Cheng H; Li H; Li Y; Li X
    Math Biosci Eng; 2024 Jan; 21(1):736-764. PubMed ID: 38303441
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 26.