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Journal Abstract Search

534 related items for PubMed ID: 29429018

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. 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
    [Abstract] [Full Text] [Related]

  • 3. GliomaDB: A Web Server for Integrating Glioma Omics Data and Interactive Analysis.
    Yang Y, Sui Y, Xie B, Qu H, Fang X.
    Genomics Proteomics Bioinformatics; 2019 Aug; 17(4):465-471. PubMed ID: 31811943
    [Abstract] [Full Text] [Related]

  • 4. The Need for Multi-Omics Biomarker Signatures in Precision Medicine.
    Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA.
    Int J Mol Sci; 2019 Sep 26; 20(19):. PubMed ID: 31561483
    [Abstract] [Full Text] [Related]

  • 5. Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations.
    Tebani A, Afonso C, Marret S, Bekri S.
    Int J Mol Sci; 2016 Sep 14; 17(9):. PubMed ID: 27649151
    [Abstract] [Full Text] [Related]

  • 6. Integrating multiple omics data for the discovery of potential Beclin-1 interactions in breast cancer.
    Chen Y, Wang X, Wang G, Li Z, Wang J, Huang L, Qin Z, Yuan X, Cheng Z, Zhang S, Yin Y, He J.
    Mol Biosyst; 2017 May 02; 13(5):991-999. PubMed ID: 28401970
    [Abstract] [Full Text] [Related]

  • 7. High-throughput «Omics» technologies: New tools for the study of triple-negative breast cancer.
    Judes G, Rifaï K, Daures M, Dubois L, Bignon YJ, Penault-Llorca F, Bernard-Gallon D.
    Cancer Lett; 2016 Nov 01; 382(1):77-85. PubMed ID: 26965997
    [Abstract] [Full Text] [Related]

  • 8. Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis.
    Kim HY, Choi HJ, Lee JY, Kong G.
    Brief Bioinform; 2020 Mar 23; 21(2):663-675. PubMed ID: 30698638
    [Abstract] [Full Text] [Related]

  • 9. Transcriptomics and epigenetic data integration learning module on Google Cloud.
    Ruprecht NA, Kennedy JD, Bansal B, Singhal S, Sens D, Maggio A, Doe V, Hawkins D, Campbel R, O'Connell K, Gill JS, Schaefer K, Singhal SK.
    Brief Bioinform; 2024 Jul 23; 25(Supplement_1):. PubMed ID: 39101486
    [Abstract] [Full Text] [Related]

  • 10. 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 14; 11(Suppl 3):71. PubMed ID: 30255801
    [Abstract] [Full Text] [Related]

  • 11. BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways.
    Kim I, Choi S, Kim S.
    BMC Bioinformatics; 2018 Feb 19; 19(Suppl 1):42. PubMed ID: 29504910
    [Abstract] [Full Text] [Related]

  • 12. 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 19; 189():74-85. PubMed ID: 32763377
    [Abstract] [Full Text] [Related]

  • 13. 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 18; 11(1):6265. PubMed ID: 33737557
    [Abstract] [Full Text] [Related]

  • 14. MethCNA: a database for integrating genomic and epigenomic data in human cancer.
    Deng G, Yang J, Zhang Q, Xiao ZX, Cai H.
    BMC Genomics; 2018 Feb 13; 19(1):138. PubMed ID: 29433427
    [Abstract] [Full Text] [Related]

  • 15. GABC: A comprehensive resource and Genome Atlas for Breast Cancer.
    Zhang Y, Wang P, Li X, Ning S, Li X, Cao Y, Chen SX.
    Int J Cancer; 2021 Feb 15; 148(4):988-994. PubMed ID: 33064305
    [Abstract] [Full Text] [Related]

  • 16. 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 20; 22(3):. PubMed ID: 34020548
    [Abstract] [Full Text] [Related]

  • 17. Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data.
    Xu Y, Dong Q, Li F, Xu Y, Hu C, Wang J, Shang D, Zheng X, Yang H, Zhang C, Shao M, Meng M, Xiong Z, Li X, Zhang Y.
    J Transl Med; 2019 Aug 06; 17(1):255. PubMed ID: 31387579
    [Abstract] [Full Text] [Related]

  • 18. LinkedOmics: analyzing multi-omics data within and across 32 cancer types.
    Vasaikar SV, Straub P, Wang J, Zhang B.
    Nucleic Acids Res; 2018 Jan 04; 46(D1):D956-D963. PubMed ID: 29136207
    [Abstract] [Full Text] [Related]

  • 19. DevOmics: an integrated multi-omics database of human and mouse early embryo.
    Yan Z, An J, Peng Y, Kong S, Liu Q, Yang M, He Q, Song S, Chen Y, Chen W, Li R, Qiao J, Yan L.
    Brief Bioinform; 2021 Nov 05; 22(6):. PubMed ID: 34097004
    [Abstract] [Full Text] [Related]

  • 20. Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.
    Rashid MM, Selvarajoo K.
    Brief Bioinform; 2024 May 23; 25(4):. PubMed ID: 38904542
    [Abstract] [Full Text] [Related]

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