These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

143 related articles for article (PubMed ID: 12416686)

  • 1. Analysis and visualization of gene expression data using self-organizing maps.
    Nikkilä J; Törönen P; Kaski S; Venna J; Castrén E; Wong G
    Neural Netw; 2002; 15(8-9):953-66. PubMed ID: 12416686
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Trustworthiness and metrics in visualizing similarity of gene expression.
    Kaski S; Nikkilä J; Oja M; Venna J; Törönen P; Castrén E
    BMC Bioinformatics; 2003 Oct; 4():48. PubMed ID: 14552657
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Clustering of gene expression data: performance and similarity analysis.
    Yin L; Huang CH; Ni J
    BMC Bioinformatics; 2006 Dec; 7 Suppl 4(Suppl 4):S19. PubMed ID: 17217511
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns.
    Herrero J; Dopazo J
    J Proteome Res; 2002; 1(5):467-70. PubMed ID: 12645919
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Improving cluster visualization in self-organizing maps: application in gene expression data analysis.
    Fernandez EA; Balzarini M
    Comput Biol Med; 2007 Dec; 37(12):1677-89. PubMed ID: 17544390
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps.
    Brameier M; Wiuf C
    J Biomed Inform; 2007 Apr; 40(2):160-73. PubMed ID: 16824804
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.
    Hsu AL; Tang SL; Halgamuge SK
    Bioinformatics; 2003 Nov; 19(16):2131-40. PubMed ID: 14594719
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study.
    Wang J; Delabie J; Aasheim H; Smeland E; Myklebost O
    BMC Bioinformatics; 2002 Nov; 3():36. PubMed ID: 12445336
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detecting clusters of different geometrical shapes in microarray gene expression data.
    Kim DW; Lee KH; Lee D
    Bioinformatics; 2005 May; 21(9):1927-34. PubMed ID: 15647300
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An improved algorithm for clustering gene expression data.
    Bandyopadhyay S; Mukhopadhyay A; Maulik U
    Bioinformatics; 2007 Nov; 23(21):2859-65. PubMed ID: 17720981
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Adaptive double self-organizing maps for clustering gene expression profiles.
    Ressom H; Wang D; Natarajan P
    Neural Netw; 2003; 16(5-6):633-40. PubMed ID: 12850017
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles.
    Luo F; Khan L; Bastani F; Yen IL; Zhou J
    Bioinformatics; 2004 Nov; 20(16):2605-17. PubMed ID: 15130935
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Minimum entropy clustering and applications to gene expression analysis.
    Li H; Zhang K; Jiang T
    Proc IEEE Comput Syst Bioinform Conf; 2004; ():142-51. PubMed ID: 16448008
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Analysis of gene expression profiles: an application of memetic algorithms to the minimum sum-of-squares clustering problem.
    Merz P
    Biosystems; 2003 Nov; 72(1-2):99-109. PubMed ID: 14642661
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A method for clustering gene expression data based on graph structure.
    Seno S; Teramoto R; Takenaka Y; Matsuda H
    Genome Inform; 2004; 15(2):151-60. PubMed ID: 15706501
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Kernel-based self-organized maps trained with supervised bias for gene expression data analysis.
    Papadimitriou S; Likothanassis SD
    J Bioinform Comput Biol; 2004 Jan; 1(4):647-80. PubMed ID: 15290758
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Application of Multi-SOM clustering approach to macrophage gene expression analysis.
    Ghouila A; Yahia SB; Malouche D; Jmel H; Laouini D; Guerfali FZ; Abdelhak S
    Infect Genet Evol; 2009 May; 9(3):328-36. PubMed ID: 18992849
    [TBL] [Abstract][Full Text] [Related]  

  • 18. pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data.
    Cao H; Amendt BA
    Biochim Biophys Acta; 2016 Nov; 1860(11 Pt B):2613-8. PubMed ID: 27288587
    [TBL] [Abstract][Full Text] [Related]  

  • 19. TimeClust: a clustering tool for gene expression time series.
    Magni P; Ferrazzi F; Sacchi L; Bellazzi R
    Bioinformatics; 2008 Feb; 24(3):430-2. PubMed ID: 18065427
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes.
    Samarasinghe S; Chaiboonchoe A
    Methods Mol Biol; 2015; 1260():285-317. PubMed ID: 25502389
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.