BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

220 related articles for article (PubMed ID: 29852967)

  • 1. Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome.
    Sun D; Li A; Tang B; Wang M
    Comput Methods Programs Biomed; 2018 Jul; 161():45-53. PubMed ID: 29852967
    [TBL] [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
    [TBL] [Abstract][Full Text] [Related]  

  • 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; 124():100-107. PubMed ID: 28627406
    [TBL] [Abstract][Full Text] [Related]  

  • 4. LSCDFS-MKL: A multiple kernel based method for lung squamous cell carcinomas disease-free survival prediction with pathological and genomic data.
    Zhang A; Li A; He J; Wang M
    J Biomed Inform; 2019 Jun; 94():103194. PubMed ID: 31048071
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning.
    Song T; Wang Y; Du W; Cao S; Tian Y; Liang Y
    J Bioinform Comput Biol; 2017 Feb; 15(1):1650037. PubMed ID: 27899048
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Cancer Survival Prediction Method Based on Graph Convolutional Network.
    Wang C; Guo J; Zhao N; Liu Y; Liu X; Liu G; Guo M
    IEEE Trans Nanobioscience; 2020 Jan; 19(1):117-126. PubMed ID: 31443039
    [TBL] [Abstract][Full Text] [Related]  

  • 7. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.
    Zou M; Liu Z; Zhang XS; Wang Y
    Bioinformatics; 2015 Oct; 31(20):3330-8. PubMed ID: 26092859
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mixture classification model based on clinical markers for breast cancer prognosis.
    Zeng T; Liu J
    Artif Intell Med; 2010; 48(2-3):129-37. PubMed ID: 20005686
    [TBL] [Abstract][Full Text] [Related]  

  • 9. GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction.
    Wang Z; Li R; Wang M; Li A
    Bioinformatics; 2021 Sep; 37(18):2963-2970. PubMed ID: 33734318
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Topologically inferring pathway activity for precise survival outcome prediction: breast cancer as a case.
    Liu W; Wang W; Tian G; Xie W; Lei L; Liu J; Huang W; Xu L; Li E
    Mol Biosyst; 2017 Feb; 13(3):537-548. PubMed ID: 28098303
    [TBL] [Abstract][Full Text] [Related]  

  • 11. eBreCaP: extreme learning-based model for breast cancer survival prediction.
    Dhillon A; Singh A
    IET Syst Biol; 2020 Jun; 14(3):160-169. PubMed ID: 32406380
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Joint analysis of histopathology image features and gene expression in breast cancer.
    Popovici V; Budinská E; Čápková L; Schwarz D; Dušek L; Feit J; Jaggi R
    BMC Bioinformatics; 2016 May; 17(1):209. PubMed ID: 27170365
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Novel Pathological Images and Genomic Data Fusion Framework for Breast Cancer Survival Prediction.
    Li S; Shi H; Sui D; Hao A; Qin H
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1384-1387. PubMed ID: 33018247
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression.
    Goli S; Mahjub H; Faradmal J; Mashayekhi H; Soltanian AR
    Comput Math Methods Med; 2016; 2016():2157984. PubMed ID: 27882074
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An ensemble machine learning approach to predict survival in breast cancer.
    Djebbari A; Liu Z; Phan S; Famili F
    Int J Comput Biol Drug Des; 2008; 1(3):275-94. PubMed ID: 20054993
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Survival differences of CIMP subtypes integrated with CNA information in human breast cancer.
    Wang H; Yan W; Zhang S; Gu Y; Wang Y; Wei Y; Liu H; Wang F; Wu Q; Zhang Y
    Oncotarget; 2017 Jul; 8(30):48807-48819. PubMed ID: 28415743
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Pathway aggregation for survival prediction via multiple kernel learning.
    Sinnott JA; Cai T
    Stat Med; 2018 Jul; 37(16):2501-2515. PubMed ID: 29664143
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis.
    Zhang Y; Yang W; Li D; Yang JY; Guan R; Yang MQ
    BMC Med Genomics; 2018 Nov; 11(Suppl 5):104. PubMed ID: 30454048
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer.
    Kim SY; Kim TR; Jeong HH; Sohn KA
    BMC Med Genomics; 2018 Sep; 11(Suppl 3):68. PubMed ID: 30255812
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multi-modal fusion network with intra- and inter-modality attention for prognosis prediction in breast cancer.
    Liu H; Shi Y; Li A; Wang M
    Comput Biol Med; 2024 Jan; 168():107796. PubMed ID: 38064843
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.