BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

379 related articles for article (PubMed ID: 32346383)

  • 1. sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and
    Das P; Roychowdhury A; Das S; Roychoudhury S; Tripathy S
    Front Genet; 2020; 11():247. PubMed ID: 32346383
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.
    Zhang Y; Deng Q; Liang W; Zou X
    Biomed Res Int; 2018; 2018():7538204. PubMed ID: 30228989
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Recursive cluster elimination (RCE) for classification and feature selection from gene expression data.
    Yousef M; Jung S; Showe LC; Showe MK
    BMC Bioinformatics; 2007 May; 8():144. PubMed ID: 17474999
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SVM-T-RFE: a novel gene selection algorithm for identifying metastasis-related genes in colorectal cancer using gene expression profiles.
    Li X; Peng S; Chen J; Lü B; Zhang H; Lai M
    Biochem Biophys Res Commun; 2012 Mar; 419(2):148-53. PubMed ID: 22306013
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data.
    Zhou X; Tuck DP
    Bioinformatics; 2007 May; 23(9):1106-14. PubMed ID: 17494773
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Ensemble Feature Learning of Genomic Data Using Support Vector Machine.
    Anaissi A; Goyal M; Catchpoole DR; Braytee A; Kennedy PJ
    PLoS One; 2016; 11(6):e0157330. PubMed ID: 27304923
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis.
    Tang Y; Zhang YQ; Huang Z
    IEEE/ACM Trans Comput Biol Bioinform; 2007; 4(3):365-81. PubMed ID: 17666757
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A comparative study of different machine learning methods on microarray gene expression data.
    Pirooznia M; Yang JY; Yang MQ; Deng Y
    BMC Genomics; 2008; 9 Suppl 1(Suppl 1):S13. PubMed ID: 18366602
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.
    Lin X; Li C; Zhang Y; Su B; Fan M; Wei H
    Molecules; 2017 Dec; 23(1):. PubMed ID: 29278382
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Efficient feature selection and classification for microarray data.
    Li Z; Xie W; Liu T
    PLoS One; 2018; 13(8):e0202167. PubMed ID: 30125332
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.
    Xi M; Sun J; Liu L; Fan F; Wu X
    Comput Math Methods Med; 2016; 2016():3572705. PubMed ID: 27642363
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.
    Zhang X; Lu X; Shi Q; Xu XQ; Leung HC; Harris LN; Iglehart JD; Miron A; Liu JS; Wong WH
    BMC Bioinformatics; 2006 Apr; 7():197. PubMed ID: 16606446
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE.
    Niijima S; Kuhara S
    BMC Bioinformatics; 2006 Dec; 7():543. PubMed ID: 17187691
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Enzyme classification using multiclass support vector machine and feature subset selection.
    Pradhan D; Padhy S; Sahoo B
    Comput Biol Chem; 2017 Oct; 70():211-219. PubMed ID: 28934693
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection.
    Mao Y; Zhou X; Pi D; Sun Y; Wong ST
    J Biomed Biotechnol; 2005 Jun; 2005(2):160-71. PubMed ID: 16046822
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Cancer survival classification using integrated data sets and intermediate information.
    Kim S; Park T; Kon M
    Artif Intell Med; 2014 Sep; 62(1):23-31. PubMed ID: 24997860
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer.
    Deng F; Zhao L; Yu N; Lin Y; Zhang L
    Lab Invest; 2024 Mar; 104(3):100320. PubMed ID: 38158124
    [TBL] [Abstract][Full Text] [Related]  

  • 18. AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM.
    Yoon S; Kim S
    BMC Med Inform Decis Mak; 2009 Nov; 9 Suppl 1(Suppl 1):S1. PubMed ID: 19891795
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Effective hybrid feature selection using different bootstrap enhances cancers classification performance.
    Abdelwahed NM; El-Tawel GS; Makhlouf MA
    BioData Min; 2022 Sep; 15(1):24. PubMed ID: 36175944
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improving the computational efficiency of recursive cluster elimination for gene selection.
    Luo LK; Huang DF; Ye LJ; Zhou QF; Shao GF; Peng H
    IEEE/ACM Trans Comput Biol Bioinform; 2011; 8(1):122-9. PubMed ID: 20479497
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.