BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

155 related articles for article (PubMed ID: 32804367)

  • 1. A Novel Computational Approach for Biomarker Detection for Gene Expression-Based Computer-Aided Diagnostic Systems for Breast Cancer.
    Al-Yousef A; Samarasinghe S
    Methods Mol Biol; 2021; 2190():195-208. PubMed ID: 32804367
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.
    Tuo Y; An N; Zhang M
    Mol Med Rep; 2018 Mar; 17(3):4281-4290. PubMed ID: 29328377
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression.
    Morais-Rodrigues F; Silv Erio-Machado R; Kato RB; Rodrigues DLN; Valdez-Baez J; Fonseca V; San EJ; Gomes LGR; Dos Santos RG; Vinicius Canário Viana M; da Cruz Ferraz Dutra J; Teixeira Dornelles Parise M; Parise D; Campos FF; de Souza SJ; Ortega JM; Barh D; Ghosh P; Azevedo VAC; Dos Santos MA
    Gene; 2020 Feb; 726():144168. PubMed ID: 31759986
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. Identification of potential biomarkers on microarray data using distributed gene selection approach.
    Shukla AK; Tripathi D
    Math Biosci; 2019 Sep; 315():108230. PubMed ID: 31326384
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.
    Chen Z; Li J; Wei L
    Artif Intell Med; 2007 Oct; 41(2):161-75. PubMed ID: 17851055
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. Improving accuracy for cancer classification with a new algorithm for genes selection.
    Zhang H; Wang H; Dai Z; Chen MS; Yuan Z
    BMC Bioinformatics; 2012 Nov; 13():298. PubMed ID: 23148517
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Hybrid Feature Selection Algorithm mRMR-ICA for Cancer Classification from Microarray Gene Expression Data.
    Wang S; Kong W; Aorigele ; Deng J; Gao S; Zeng W
    Comb Chem High Throughput Screen; 2018; 21(6):420-430. PubMed ID: 29852866
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identifying cancer biomarkers by network-constrained support vector machines.
    Chen L; Xuan J; Riggins RB; Clarke R; Wang Y
    BMC Syst Biol; 2011 Oct; 5():161. PubMed ID: 21992556
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features.
    Aswathy MA; Jagannath M
    Med Biol Eng Comput; 2021 Sep; 59(9):1773-1783. PubMed ID: 34302269
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer.
    Staiger C; Cadot S; Kooter R; Dittrich M; Müller T; Klau GW; Wessels LF
    PLoS One; 2012; 7(4):e34796. PubMed ID: 22558100
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer.
    Chereda H; Leha A; Beißbarth T
    Artif Intell Med; 2024 May; 151():102840. PubMed ID: 38658129
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identification of co-expression modules and potential biomarkers of breast cancer by WGCNA.
    Jia R; Zhao H; Jia M
    Gene; 2020 Aug; 750():144757. PubMed ID: 32387385
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series.
    Gálvez JM; Castillo D; Herrera LJ; San Román B; Valenzuela O; Ortuño FM; Rojas I
    PLoS One; 2018; 13(5):e0196836. PubMed ID: 29750795
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Identifying condition specific key genes from basal-like breast cancer gene expression data.
    Maind A; Raut S
    Comput Biol Chem; 2019 Feb; 78():367-374. PubMed ID: 30655072
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using a single neuron as a marker selector: a breast cancer case study.
    Blazadonakis ME; Perperoglou A; Zervakis M
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():4219-22. PubMed ID: 18002933
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.