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Journal Abstract Search

1712 related items for PubMed ID: 26048077

  • 1. 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
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  • 3. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
    Liu C, Wang X, Genchev GZ, Lu H.
    Methods; 2017 Jul 15; 124():100-107. PubMed ID: 28627406
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  • 6. 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 15; 189():74-85. PubMed ID: 32763377
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  • 7. 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
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  • 8. Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
    Suo C, Hrydziuszko O, Lee D, Pramana S, Saputra D, Joshi H, Calza S, Pawitan Y.
    Bioinformatics; 2015 Aug 15; 31(16):2607-13. PubMed ID: 25810432
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  • 10. 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 15; 169(3):625-632. PubMed ID: 29429018
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  • 11. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.
    Verbeke LP, Van den Eynden J, Fierro AC, Demeester P, Fostier J, Marchal K.
    PLoS One; 2015 Jun 15; 10(7):e0133503. PubMed ID: 26217958
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  • 12. A plea for taking all available clinical information into account when assessing the predictive value of omics data.
    Volkmann A, De Bin R, Sauerbrei W, Boulesteix AL.
    BMC Med Res Methodol; 2019 Jul 24; 19(1):162. PubMed ID: 31340753
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  • 13. Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers.
    Xu L, Fengji L, Changning L, Liangcai Z, Yinghui L, Yu L, Shanguang C, Jianghui X.
    PLoS One; 2015 Jul 24; 10(11):e0142433. PubMed ID: 26606135
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  • 14. A hybrid approach to survival model building using integration of clinical and molecular information in censored data.
    Choi I, Kattan MW, Wells BJ, Yu C.
    IEEE/ACM Trans Comput Biol Bioinform; 2012 Jul 24; 9(4):1091-1105. PubMed ID: 22350208
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  • 15. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.
    Kim D, Joung JG, Sohn KA, Shin H, Park YR, Ritchie MD, Kim JH.
    J Am Med Inform Assoc; 2015 Jan 24; 22(1):109-20. PubMed ID: 25002459
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  • 16. Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis.
    Mo W, Ding Y, Zhao S, Zou D, Ding X.
    PLoS One; 2020 Jan 24; 15(11):e0241924. PubMed ID: 33170908
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  • 19. Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas.
    Song YZ, Li X, Li W, Wang Z, Li K, Xie FL, Zhang F.
    World J Gastroenterol; 2018 Jul 28; 24(28):3145-3154. PubMed ID: 30065560
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  • 20. Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis.
    Rajamani D, Bhasin MK.
    Genome Med; 2016 May 03; 8(1):38. PubMed ID: 27137215
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