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 *

354 related articles for article (PubMed ID: 26390495)

  • 1. Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.
    Ang JC; Mirzal A; Haron H; Hamed HN
    IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(5):971-989. PubMed ID: 26390495
    [TBL] [Abstract][Full Text] [Related]  

  • 2. The feature selection bias problem in relation to high-dimensional gene data.
    Krawczuk J; Łukaszuk T
    Artif Intell Med; 2016 Jan; 66():63-71. PubMed ID: 26674595
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification and analysis of driver missense mutations using rotation forest with feature selection.
    Du X; Cheng J
    Biomed Res Int; 2014; 2014():905951. PubMed ID: 25250338
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Semi-supervised and unsupervised extreme learning machines.
    Huang G; Song S; Gupta JN; Wu C
    IEEE Trans Cybern; 2014 Dec; 44(12):2405-17. PubMed ID: 25415946
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Feature selection in supervised saliency prediction.
    Liang M; Hu X
    IEEE Trans Cybern; 2015 May; 45(5):900-12. PubMed ID: 25122849
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Empirical study of supervised gene screening.
    Ma S
    BMC Bioinformatics; 2006 Dec; 7():537. PubMed ID: 17176468
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Feature selection and nearest centroid classification for protein mass spectrometry.
    Levner I
    BMC Bioinformatics; 2005 Mar; 6():68. PubMed ID: 15788095
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Cuckoo search optimisation for feature selection in cancer classification: a new approach.
    Gunavathi C; Premalatha K
    Int J Data Min Bioinform; 2015; 13(3):248-65. PubMed ID: 26547979
    [TBL] [Abstract][Full Text] [Related]  

  • 9. New gene selection method for multiclass tumor classification by class centroid.
    Shen Q; Shi WM; Kong W
    J Biomed Inform; 2009 Feb; 42(1):59-65. PubMed ID: 18835752
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Chaotic genetic algorithm for gene selection and classification problems.
    Chuang LY; Yang CS; Li JC; Yang CH
    OMICS; 2009 Oct; 13(5):407-20. PubMed ID: 19594377
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Laplacian linear discriminant analysis approach to unsupervised feature selection.
    Niijima S; Okuno Y
    IEEE/ACM Trans Comput Biol Bioinform; 2009; 6(4):605-14. PubMed ID: 19875859
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Neurodynamics-driven holistic approaches to semi-supervised feature selection.
    Wang Y; Wang J
    Neural Netw; 2023 Jan; 157():377-386. PubMed ID: 36410303
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis.
    Li GZ; Bu HL; Yang MQ; Zeng XQ; Yang JY
    BMC Genomics; 2008 Sep; 9 Suppl 2(Suppl 2):S24. PubMed ID: 18831790
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer.
    Bhadra T; Mallik S; Hasan N; Zhao Z
    BMC Bioinformatics; 2022 Apr; 23(Suppl 3):153. PubMed ID: 35484501
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression.
    Li T; Zhang C; Ogihara M
    Bioinformatics; 2004 Oct; 20(15):2429-37. PubMed ID: 15087314
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Discriminative semi-supervised feature selection via manifold regularization.
    Xu Z; King I; Lyu MR; Jin R
    IEEE Trans Neural Netw; 2010 Jul; 21(7):1033-47. PubMed ID: 20570772
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Locality preserving score for joint feature weights learning.
    Yan H; Yang J
    Neural Netw; 2015 Sep; 69():126-34. PubMed ID: 26113239
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Feature selection in gene expression data using principal component analysis and rough set theory.
    Mishra D; Dash R; Rath AK; Acharya M
    Adv Exp Med Biol; 2011; 696():91-100. PubMed ID: 21431550
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A combination of rough-based feature selection and RBF neural network for classification using gene expression data.
    Chiang JH; Ho SH
    IEEE Trans Nanobioscience; 2008 Mar; 7(1):91-9. PubMed ID: 18334459
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Pairwise Constraint-Guided Sparse Learning for Feature Selection.
    Liu M; Zhang D
    IEEE Trans Cybern; 2016 Jan; 46(1):298-310. PubMed ID: 26151948
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.