BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

279 related articles for article (PubMed ID: 31500324)

  • 1. Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.
    Sompairac N; Nazarov PV; Czerwinska U; Cantini L; Biton A; Molkenov A; Zhumadilov Z; Barillot E; Radvanyi F; Gorban A; Kairov U; Zinovyev A
    Int J Mol Sci; 2019 Sep; 20(18):. PubMed ID: 31500324
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.
    Xie J; Douglas PK; Wu YN; Brody AL; Anderson AE
    J Neurosci Methods; 2017 Apr; 282():81-94. PubMed ID: 28322859
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Integrative Hypergraph Regularization Principal Component Analysis for Sample Clustering and Co-Expression Genes Network Analysis on Multi-Omics Data.
    Wu MJ; Gao YL; Liu JX; Zheng CH; Wang J
    IEEE J Biomed Health Inform; 2020 Jun; 24(6):1823-1834. PubMed ID: 31634852
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients.
    Nazarov PV; Wienecke-Baldacchino AK; Zinovyev A; Czerwińska U; Muller A; Nashan D; Dittmar G; Azuaje F; Kreis S
    BMC Med Genomics; 2019 Sep; 12(1):132. PubMed ID: 31533822
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets.
    Yao F; Coquery J; Lê Cao KA
    BMC Bioinformatics; 2012 Feb; 13():24. PubMed ID: 22305354
    [TBL] [Abstract][Full Text] [Related]  

  • 6. PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.
    Lemsara A; Ouadfel S; Fröhlich H
    BMC Bioinformatics; 2020 Apr; 21(1):146. PubMed ID: 32299344
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multi-omic and multi-view clustering algorithms: review and cancer benchmark.
    Rappoport N; Shamir R
    Nucleic Acids Res; 2018 Nov; 46(20):10546-10562. PubMed ID: 30295871
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey.
    Liu JX; Wang D; Gao YL; Zheng CH; Xu Y; Yu J
    IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(3):974-987. PubMed ID: 28186906
    [TBL] [Abstract][Full Text] [Related]  

  • 9. scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.
    Qiu Y; Guo D; Zhao P; Zou Q
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38754408
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks.
    Wang P; Gao L; Hu Y; Li F
    BMC Bioinformatics; 2018 Oct; 19(1):394. PubMed ID: 30373534
    [TBL] [Abstract][Full Text] [Related]  

  • 11. MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics.
    Liu Y; Smirnov K; Lucio M; Gougeon RD; Alexandre H; Schmitt-Kopplin P
    BMC Bioinformatics; 2016 Mar; 17():114. PubMed ID: 26936354
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Assessing reproducibility of matrix factorization methods in independent transcriptomes.
    Cantini L; Kairov U; de Reyniès A; Barillot E; Radvanyi F; Zinovyev A
    Bioinformatics; 2019 Nov; 35(21):4307-4313. PubMed ID: 30938767
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Analysis of fMRI data by blind separation into independent spatial components.
    McKeown MJ; Makeig S; Brown GG; Jung TP; Kindermann SS; Bell AJ; Sejnowski TJ
    Hum Brain Mapp; 1998; 6(3):160-88. PubMed ID: 9673671
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.
    Park M; Kim D; Moon K; Park T
    Int J Mol Sci; 2020 Nov; 21(21):. PubMed ID: 33147797
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.
    Oshternian SR; Loipfinger S; Bhattacharya A; Fehrmann RSN
    BMC Bioinformatics; 2024 Apr; 25(1):167. PubMed ID: 38671342
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
    Duan R; Gao L; Gao Y; Hu Y; Xu H; Huang M; Song K; Wang H; Dong Y; Jiang C; Zhang C; Jia S
    PLoS Comput Biol; 2021 Aug; 17(8):e1009224. PubMed ID: 34383739
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.
    Ashenova A; Daniyarov A; Molkenov A; Sharip A; Zinovyev A; Kairov U
    Front Genet; 2021; 12():683632. PubMed ID: 34795689
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets.
    Tresch MC; Cheung VC; d'Avella A
    J Neurophysiol; 2006 Apr; 95(4):2199-212. PubMed ID: 16394079
    [TBL] [Abstract][Full Text] [Related]  

  • 19. LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data.
    Liu C; JaJa J; Pessoa L
    Neuroimage; 2018 Apr; 169():363-373. PubMed ID: 29246846
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 14.