Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

216 related articles for article (PubMed ID: 24497942)

  • 1. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.
    Park C; Ahn J; Kim H; Park S
    PLoS One; 2014; 9(1):e86309. PubMed ID: 24497942
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Semi-supervised learning improves gene expression-based prediction of cancer recurrence.
    Shi M; Zhang B
    Bioinformatics; 2011 Nov; 27(21):3017-23. PubMed ID: 21893520
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Integrative gene network construction for predicting a set of complementary prostate cancer genes.
    Ahn J; Yoon Y; Park C; Shin E; Park S
    Bioinformatics; 2011 Jul; 27(13):1846-53. PubMed ID: 21551151
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.
    You ZH; Yin Z; Han K; Huang DS; Zhou X
    BMC Bioinformatics; 2010 Jun; 11():343. PubMed ID: 20573270
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A unified deep semi-supervised graph learning scheme based on nodes re-weighting and manifold regularization.
    Dornaika F; Bi J; Zhang C
    Neural Netw; 2023 Jan; 158():188-196. PubMed ID: 36462365
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A distributed semi-supervised learning algorithm based on manifold regularization using wavelet neural network.
    Xie J; Liu S; Dai H
    Neural Netw; 2019 Oct; 118():300-309. PubMed ID: 31330270
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prognostic outcome prediction by semi-supervised least squares classification.
    Shi M; Sheng Z; Tang H
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33094318
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identification of functional CNV region networks using a CNV-gene mapping algorithm in a genome-wide scale.
    Park C; Ahn J; Yoon Y; Park S
    Bioinformatics; 2012 Aug; 28(15):2045-51. PubMed ID: 22652832
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.
    Chen K; Wang S
    IEEE Trans Pattern Anal Mach Intell; 2011 Jan; 33(1):129-43. PubMed ID: 20421671
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Supervised, semi-supervised and unsupervised inference of gene regulatory networks.
    Maetschke SR; Madhamshettiwar PB; Davis MJ; Ragan MA
    Brief Bioinform; 2014 Mar; 15(2):195-211. PubMed ID: 23698722
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Supervised inference of gene regulatory networks from positive and unlabeled examples.
    Mordelet F; Vert JP
    Methods Mol Biol; 2013; 939():47-58. PubMed ID: 23192540
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Graph Convolution Networks with manifold regularization for semi-supervised learning.
    Kejani MT; Dornaika F; Talebi H
    Neural Netw; 2020 Jul; 127():160-167. PubMed ID: 32361546
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SemiBoost: boosting for semi-supervised learning.
    Mallapragada PK; Jin R; Jain AK; Liu Y
    IEEE Trans Pattern Anal Mach Intell; 2009 Nov; 31(11):2000-14. PubMed ID: 19762927
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.
    Liang Y; Chai H; Liu XY; Xu ZB; Zhang H; Leung KS
    BMC Med Genomics; 2016 Mar; 9():11. PubMed ID: 26932592
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Cancer module genes ranking using kernelized score functions.
    Re M; Valentini G
    BMC Bioinformatics; 2012; 13 Suppl 14(Suppl 14):S3. PubMed ID: 23095178
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.
    Gliozzo J; Perlasca P; Mesiti M; Casiraghi E; Vallacchi V; Vergani E; Frasca M; Grossi G; Petrini A; Re M; Paccanaro A; Valentini G
    Sci Rep; 2020 Feb; 10(1):3612. PubMed ID: 32107391
    [TBL] [Abstract][Full Text] [Related]  

  • 17. BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.
    Shi X; Wang X; Shajahan A; Hilakivi-Clarke L; Clarke R; Xuan J
    BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S10. PubMed ID: 26099273
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.
    She Q; Zou J; Luo Z; Nguyen T; Li R; Zhang Y
    Med Biol Eng Comput; 2020 Sep; 58(9):2119-2130. PubMed ID: 32676841
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.
    Lu X; Li X; Liu P; Qian X; Miao Q; Peng S
    Molecules; 2018 Jan; 23(2):. PubMed ID: 29364829
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.
    Peng Y; Lu BL; Wang S
    Neural Netw; 2015 May; 65():1-17. PubMed ID: 25634552
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.