BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

128 related articles for article (PubMed ID: 35123235)

  • 21. The relevance of flow cytometry for biochemical analysis.
    O'Connor JE; Callaghan RC; Escudero M; Herrera G; Martínez A; Monteiro MD; Montolíu H
    IUBMB Life; 2001 Apr; 51(4):231-9. PubMed ID: 11569917
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities.
    Ghannam RB; Schaerer LG; Butler TM; Techtmann SM
    mSphere; 2020 Jan; 5(1):. PubMed ID: 31996419
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.
    Diggins KE; Ferrell PB; Irish JM
    Methods; 2015 Jul; 82():55-63. PubMed ID: 25979346
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Characterizing Microbiome Dynamics - Flow Cytometry Based Workflows from Pure Cultures to Natural Communities.
    Lambrecht J; Schattenberg F; Harms H; Mueller S
    J Vis Exp; 2018 Jul; (137):. PubMed ID: 30059034
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.
    Busato S; Gordon M; Chaudhari M; Jensen I; Akyol T; Andersen S; Williams C
    Curr Opin Plant Biol; 2023 Feb; 71():102326. PubMed ID: 36538837
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.
    Dutta A; Goldman T; Keating J; Burke E; Williamson N; Dirmeier R; Bowman JS
    Microbiol Spectr; 2022 Feb; 10(1):e0190921. PubMed ID: 35138192
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Machine learning predicts ecological risks of nanoparticles to soil microbial communities.
    Xu N; Kang J; Ye Y; Zhang Q; Ke M; Wang Y; Zhang Z; Lu T; Peijnenburg WJGM; Josep Penuelas ; Bao G; Qian H
    Environ Pollut; 2022 Aug; 307():119528. PubMed ID: 35623569
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Learning Single-Cell Distances from Cytometry Data.
    Nguyen B; Rubbens P; Kerckhof FM; Boon N; De Baets B; Waegeman W
    Cytometry A; 2019 Jul; 95(7):782-791. PubMed ID: 31099963
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Metabolome of human gut microbiome is predictive of host dysbiosis.
    Larsen PE; Dai Y
    Gigascience; 2015; 4():42. PubMed ID: 26380076
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Machine learning predicts the impact of antibiotic properties on the composition and functioning of bacterial community in aquatic habitats.
    Kang J; Zhang Z; Chen Y; Zhou Z; Zhang J; Xu N; Zhang Q; Lu T; Peijnenburg WJGM; Qian H
    Sci Total Environ; 2022 Jul; 828():154412. PubMed ID: 35276139
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Application of Machine Learning for Cytometry Data.
    Hu Z; Bhattacharya S; Butte AJ
    Front Immunol; 2021; 12():787574. PubMed ID: 35046945
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Viability of amoebae, fungal conidia, and yeasts: rapid assessment by flow cytometry.
    Noble-Wang JA; Zhang S; Price D; Ahearn DG
    Methods Mol Biol; 2004; 268():153-61. PubMed ID: 15156027
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome.
    Esvap E; Ulgen KO
    ACS Synth Biol; 2021 Sep; 10(9):2121-2137. PubMed ID: 34402617
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Taxonomic and Functional Dysregulation in Salivary Microbiomes During Oral Carcinogenesis.
    Chen JW; Wu JH; Chiang WF; Chen YL; Wu WS; Wu LW
    Front Cell Infect Microbiol; 2021; 11():663068. PubMed ID: 34604102
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes.
    Dhoble AS; Lahiri P; Bhalerao KD
    J Biol Eng; 2018; 12():19. PubMed ID: 30220912
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Meta-analysis of 16S rRNA Microbial Data Identified Distinctive and Predictive Microbiota Dysbiosis in Colorectal Carcinoma Adjacent Tissue.
    Mo Z; Huang P; Yang C; Xiao S; Zhang G; Ling F; Li L
    mSystems; 2020 Apr; 5(2):. PubMed ID: 32291348
    [TBL] [Abstract][Full Text] [Related]  

  • 37. EXPERT: transfer learning-enabled context-aware microbial community classification.
    Chong H; Zha Y; Yu Q; Cheng M; Xiong G; Wang N; Huang X; Huang S; Sun C; Wu S; Chen WH; Coelho LP; Ning K
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36124759
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Application of machine learning techniques for creating urban microbial fingerprints.
    Ryan FJ
    Biol Direct; 2019 Aug; 14(1):13. PubMed ID: 31420049
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Quantitative microbiome profiling links gut community variation to microbial load.
    Vandeputte D; Kathagen G; D'hoe K; Vieira-Silva S; Valles-Colomer M; Sabino J; Wang J; Tito RY; De Commer L; Darzi Y; Vermeire S; Falony G; Raes J
    Nature; 2017 Nov; 551(7681):507-511. PubMed ID: 29143816
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry.
    Rubbens P; Schmidt ML; Props R; Biddanda BA; Boon N; Waegeman W; Denef VJ
    mSystems; 2019 Sep; 4(5):. PubMed ID: 31506260
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.