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Journal Abstract Search

148 related items for PubMed ID: 39333869

  • 1. Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP.
    Edwards H, Zavorskas J, Huso W, Doan AG, Silbiger C, Harris S, Srivastava R, Marten MR.
    BMC Bioinformatics; 2024 Sep 27; 25(1):312. PubMed ID: 39333869
    [Abstract] [Full Text] [Related]

  • 2. Dynamic Transcriptomic and Phosphoproteomic Analysis During Cell Wall Stress in Aspergillus nidulans.
    Chelius C, Huso W, Reese S, Doan A, Lincoln S, Lawson K, Tran B, Purohit R, Glaros T, Srivastava R, Harris SD, Marten MR.
    Mol Cell Proteomics; 2020 Aug 27; 19(8):1310-1329. PubMed ID: 32430394
    [Abstract] [Full Text] [Related]

  • 3. Integrative Exploratory Analysis of Two or More Genomic Datasets.
    Meng C, Culhane A.
    Methods Mol Biol; 2016 Aug 27; 1418():19-38. PubMed ID: 27008008
    [Abstract] [Full Text] [Related]

  • 4. The MathIOmica Toolbox: General Analysis Utilities for Dynamic Omics Datasets.
    Mias GI, Zheng M.
    Curr Protoc Bioinformatics; 2020 Mar 27; 69(1):e91. PubMed ID: 31851777
    [Abstract] [Full Text] [Related]

  • 5. FunRich: An open access standalone functional enrichment and interaction network analysis tool.
    Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A, Hill AF, Stroud DA, Ryan MT, Agbinya JI, Mariadason JM, Burgess AW, Mathivanan S.
    Proteomics; 2015 Aug 27; 15(15):2597-601. PubMed ID: 25921073
    [Abstract] [Full Text] [Related]

  • 6. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data.
    Kuo TC, Tian TF, Tseng YJ.
    BMC Syst Biol; 2013 Jul 23; 7():64. PubMed ID: 23875761
    [Abstract] [Full Text] [Related]

  • 7. Directional integration and pathway enrichment analysis for multi-omics data.
    Slobodyanyuk M, Bahcheli AT, Klein ZP, Bayati M, Strug LJ, Reimand J.
    Nat Commun; 2024 Jul 07; 15(1):5690. PubMed ID: 38971800
    [Abstract] [Full Text] [Related]

  • 8. Development and validation of a custom microarray for global transcriptome profiling of the fungus Aspergillus nidulans.
    Deloménie C, Grentzmann G, Oestreicher N, Mesnage R, Vélot C.
    Curr Genet; 2016 Nov 07; 62(4):897-910. PubMed ID: 27038308
    [Abstract] [Full Text] [Related]

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  • 12. A consensus multi-view multi-objective gene selection approach for improved sample classification.
    Acharya S, Cui L, Pan Y.
    BMC Bioinformatics; 2020 Sep 17; 21(Suppl 13):386. PubMed ID: 32938388
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  • 13. GOAT: efficient and robust identification of gene set enrichment.
    Koopmans F.
    Commun Biol; 2024 Jun 19; 7(1):744. PubMed ID: 38898151
    [Abstract] [Full Text] [Related]

  • 14. pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge.
    Wachter A, Beißbarth T.
    Bioinformatics; 2015 Sep 15; 31(18):3072-4. PubMed ID: 26002883
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  • 15. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.
    Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N.
    BMC Bioinformatics; 2024 Jul 11; 25(1):234. PubMed ID: 38992584
    [Abstract] [Full Text] [Related]

  • 16. ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities.
    Li D, Min Z, Guo J, Chen Y, Zhang W.
    Bioinformatics; 2024 Aug 02; 40(8):. PubMed ID: 39128019
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  • 18. ivTerm-An R package for interactive visualization of functional analysis results of meta-omics data.
    Dong X, Xue H, Wei C.
    J Cell Biochem; 2021 Oct 02; 122(10):1428-1434. PubMed ID: 34132422
    [Abstract] [Full Text] [Related]

  • 19. ShinyOmics: collaborative exploration of omics-data.
    Surujon D, van Opijnen T.
    BMC Bioinformatics; 2020 Jan 17; 21(1):22. PubMed ID: 31952481
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  • 20. Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups.
    Wiebe DS, Omelyanchuk NA, Mukhin AM, Grosse I, Lashin SA, Zemlyanskaya EV, Mironova VV.
    Genes (Basel); 2020 Apr 17; 11(4):. PubMed ID: 32316383
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