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 *

230 related articles for article (PubMed ID: 30799483)

  • 41. SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.
    Peng H; Fan W; Fang C; Gao W; Li Y
    Comput Math Methods Med; 2021; 2021():6842752. PubMed ID: 34646337
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations.
    Shin D; Lee W; Lee JH; Bang D
    Sci Adv; 2019 May; 5(5):eaav2249. PubMed ID: 31106268
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.
    Bakken TE; Hodge RD; Miller JA; Yao Z; Nguyen TN; Aevermann B; Barkan E; Bertagnolli D; Casper T; Dee N; Garren E; Goldy J; Graybuck LT; Kroll M; Lasken RS; Lathia K; Parry S; Rimorin C; Scheuermann RH; Schork NJ; Shehata SI; Tieu M; Phillips JW; Bernard A; Smith KA; Zeng H; Lein ES; Tasic B
    PLoS One; 2018; 13(12):e0209648. PubMed ID: 30586455
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Single-Cell RNA Profiling of Glomerular Cells Shows Dynamic Changes in Experimental Diabetic Kidney Disease.
    Fu J; Akat KM; Sun Z; Zhang W; Schlondorff D; Liu Z; Tuschl T; Lee K; He JC
    J Am Soc Nephrol; 2019 Apr; 30(4):533-545. PubMed ID: 30846559
    [TBL] [Abstract][Full Text] [Related]  

  • 45. A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.
    Franks JM; Martyanov V; Cai G; Wang Y; Li Z; Wood TA; Whitfield ML
    Arthritis Rheumatol; 2019 Oct; 71(10):1701-1710. PubMed ID: 30920766
    [TBL] [Abstract][Full Text] [Related]  

  • 46. dropClust: efficient clustering of ultra-large scRNA-seq data.
    Sinha D; Kumar A; Kumar H; Bandyopadhyay S; Sengupta D
    Nucleic Acids Res; 2018 Apr; 46(6):e36. PubMed ID: 29361178
    [TBL] [Abstract][Full Text] [Related]  

  • 47. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.
    Müller S; Cho A; Liu SJ; Lim DA; Diaz A
    Bioinformatics; 2018 Sep; 34(18):3217-3219. PubMed ID: 29897414
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis.
    Wu H; Kirita Y; Donnelly EL; Humphreys BD
    J Am Soc Nephrol; 2019 Jan; 30(1):23-32. PubMed ID: 30510133
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.
    Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S
    Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data.
    Lindeman I; Stubbington MJT
    Methods Mol Biol; 2019; 1935():223-249. PubMed ID: 30758830
    [TBL] [Abstract][Full Text] [Related]  

  • 51. A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.
    Xiao Y; Wu J; Lin Z; Zhao X
    Comput Methods Programs Biomed; 2018 Nov; 166():99-105. PubMed ID: 30415723
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Using neural networks for reducing the dimensions of single-cell RNA-Seq data.
    Lin C; Jain S; Kim H; Bar-Joseph Z
    Nucleic Acids Res; 2017 Sep; 45(17):e156. PubMed ID: 28973464
    [TBL] [Abstract][Full Text] [Related]  

  • 53. BEARscc determines robustness of single-cell clusters using simulated technical replicates.
    Severson DT; Owen RP; White MJ; Lu X; Schuster-Böckler B
    Nat Commun; 2018 Mar; 9(1):1187. PubMed ID: 29567991
    [TBL] [Abstract][Full Text] [Related]  

  • 54. scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression.
    Sun H; Zhou Y; Fei L; Chen H; Guo G
    Methods Mol Biol; 2019; 1935():91-96. PubMed ID: 30758821
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Single cell RNA-seq data clustering using TF-IDF based methods.
    Moussa M; Măndoiu II
    BMC Genomics; 2018 Aug; 19(Suppl 6):569. PubMed ID: 30367575
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts.
    Ntranos V; Kamath GM; Zhang JM; Pachter L; Tse DN
    Genome Biol; 2016 May; 17(1):112. PubMed ID: 27230763
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Putative cell type discovery from single-cell gene expression data.
    Miao Z; Moreno P; Huang N; Papatheodorou I; Brazma A; Teichmann SA
    Nat Methods; 2020 Jun; 17(6):621-628. PubMed ID: 32424270
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Navigating the Depths and Avoiding the Shallows of Pancreatic Islet Cell Transcriptomes.
    Mawla AM; Huising MO
    Diabetes; 2019 Jul; 68(7):1380-1393. PubMed ID: 31221802
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Statistical significance of cluster membership for unsupervised evaluation of cell identities.
    Chung NC
    Bioinformatics; 2020 May; 36(10):3107-3114. PubMed ID: 32142108
    [TBL] [Abstract][Full Text] [Related]  

  • 60. scMatch: a single-cell gene expression profile annotation tool using reference datasets.
    Hou R; Denisenko E; Forrest ARR
    Bioinformatics; 2019 Nov; 35(22):4688-4695. PubMed ID: 31028376
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 12.