BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

170 related articles for article (PubMed ID: 26844019)

  • 1. Cross-platform normalization of microarray and RNA-seq data for machine learning applications.
    Thompson JA; Tan J; Greene CS
    PeerJ; 2016; 4():e1621. PubMed ID: 26844019
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.
    Foltz SM; Greene CS; Taroni JN
    Commun Biol; 2023 Feb; 6(1):222. PubMed ID: 36841852
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.
    Franks JM; Cai G; Whitfield ML
    Bioinformatics; 2018 Jun; 34(11):1868-1874. PubMed ID: 29360996
    [TBL] [Abstract][Full Text] [Related]  

  • 4. MLSeq: Machine learning interface for RNA-sequencing data.
    Goksuluk D; Zararsiz G; Korkmaz S; Eldem V; Zararsiz GE; Ozcetin E; Ozturk A; Karaagaoglu AE
    Comput Methods Programs Biomed; 2019 Jul; 175():223-231. PubMed ID: 31104710
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.
    Cao X; Xing L; Majd E; He H; Gu J; Zhang X
    Front Genet; 2022; 13():836798. PubMed ID: 35281805
    [TBL] [Abstract][Full Text] [Related]  

  • 6. How does normalization impact RNA-seq disease diagnosis?
    Han H; Men K
    J Biomed Inform; 2018 Sep; 85():80-92. PubMed ID: 30041017
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era.
    Peterson RA; Cavanaugh JE
    J Appl Stat; 2020; 47(13-15):2312-2327. PubMed ID: 35707424
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.
    Smith AM; Walsh JR; Long J; Davis CB; Henstock P; Hodge MR; Maciejewski M; Mu XJ; Ra S; Zhao S; Ziemek D; Fisher CK
    BMC Bioinformatics; 2020 Mar; 21(1):119. PubMed ID: 32197580
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods.
    Quinn TP; Crowley TM; Richardson MF
    BMC Bioinformatics; 2018 Jul; 19(1):274. PubMed ID: 30021534
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies.
    Li X; Cooper NGF; O'Toole TE; Rouchka EC
    BMC Genomics; 2020 Jan; 21(1):75. PubMed ID: 31992223
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.
    Lin Y; Golovnina K; Chen ZX; Lee HN; Negron YL; Sultana H; Oliver B; Harbison ST
    BMC Genomics; 2016 Jan; 17():28. PubMed ID: 26732976
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Systematic comparison of RNA-Seq normalization methods using measurement error models.
    Sun Z; Zhu Y
    Bioinformatics; 2012 Oct; 28(20):2584-91. PubMed ID: 22914217
    [TBL] [Abstract][Full Text] [Related]  

  • 13. NormExpression: An R Package to Normalize Gene Expression Data Using Evaluated Methods.
    Wu Z; Liu W; Jin X; Ji H; Wang H; Glusman G; Robinson M; Liu L; Ruan J; Gao S
    Front Genet; 2019; 10():400. PubMed ID: 31114611
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.
    Shahriyari L
    Brief Bioinform; 2019 May; 20(3):985-994. PubMed ID: 29112707
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluation of Normalization Methods for RNA-Seq Gene Expression Estimation.
    Wu PY; Phan JH; Zhou F; Wang MD
    IEEE Int Conf Bioinform Biomed Workshops; 2011 Nov; 2011():50-57. PubMed ID: 27532058
    [TBL] [Abstract][Full Text] [Related]  

  • 16. visnormsc: A Graphical User Interface to Normalize Single-cell RNA Sequencing Data.
    Tang L; Zhou N
    Interdiscip Sci; 2018 Sep; 10(3):636-640. PubMed ID: 29280088
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of normalization methods in mammalian microRNA-Seq data.
    Garmire LX; Subramaniam S
    RNA; 2012 Jun; 18(6):1279-88. PubMed ID: 22532701
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data.
    Akter S; Xu D; Nagel SC; Bromfield JJ; Pelch K; Wilshire GB; Joshi T
    Front Genet; 2019; 10():766. PubMed ID: 31552087
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine.
    Xu J; Gong B; Wu L; Thakkar S; Hong H; Tong W
    Pharmaceutics; 2016 Mar; 8(1):. PubMed ID: 26999190
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Compendiums of cancer transcriptomes for machine learning applications.
    Lim SB; Tan SJ; Lim WT; Lim CT
    Sci Data; 2019 Oct; 6(1):194. PubMed ID: 31594947
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.