BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

120 related articles for article (PubMed ID: 38940155)

  • 1. REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.
    Yang Y; Pe'er D
    Bioinformatics; 2024 Jun; 40(Supplement_1):i567-i575. PubMed ID: 38940155
    [TBL] [Abstract][Full Text] [Related]  

  • 2. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.
    Kähärä J; Lähdesmäki H
    Bioinformatics; 2015 Sep; 31(17):2852-9. PubMed ID: 25957350
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.
    Liu S; Zibetti C; Wan J; Wang G; Blackshaw S; Qian J
    BMC Bioinformatics; 2017 Jul; 18(1):355. PubMed ID: 28750606
    [TBL] [Abstract][Full Text] [Related]  

  • 4. COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.
    Ha N; Polychronidou M; Lohmann I
    PLoS One; 2012; 7(12):e52055. PubMed ID: 23272209
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.
    Malekpour SA; Haghverdi L; Sadeghi M
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38653489
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS.
    Trimbour R; Deutschmann IM; Cantini L
    Bioinformatics; 2024 May; 40(5):. PubMed ID: 38460192
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility.
    Chen X; Yu B; Carriero N; Silva C; Bonneau R
    Nucleic Acids Res; 2017 May; 45(8):4315-4329. PubMed ID: 28334916
    [TBL] [Abstract][Full Text] [Related]  

  • 8. DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.
    Liu Q; Hua K; Zhang X; Wong WH; Jiang R
    Genomics Proteomics Bioinformatics; 2022 Jun; 20(3):496-507. PubMed ID: 35293310
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.
    Panchy NL; Lloyd JP; Shiu SH
    BMC Genomics; 2020 Feb; 21(1):159. PubMed ID: 32054475
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.
    Greenside P; Shimko T; Fordyce P; Kundaje A
    Bioinformatics; 2018 Sep; 34(17):i629-i637. PubMed ID: 30423062
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.
    Tsai ZT; Shiu SH; Tsai HK
    PLoS Comput Biol; 2015 Aug; 11(8):e1004418. PubMed ID: 26291518
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Efficient inference for sparse latent variable models of transcriptional regulation.
    Dai Z; Iqbal M; Lawrence ND; Rattray M
    Bioinformatics; 2017 Dec; 33(23):3776-3783. PubMed ID: 28961802
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data.
    He X; Chen CC; Hong F; Fang F; Sinha S; Ng HH; Zhong S
    PLoS One; 2009 Dec; 4(12):e8155. PubMed ID: 19956545
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.
    Marbach D; Roy S; Ay F; Meyer PE; Candeias R; Kahveci T; Bristow CA; Kellis M
    Genome Res; 2012 Jul; 22(7):1334-49. PubMed ID: 22456606
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data.
    Schultheis H; Bentsen M; Heger V; Looso M
    Sci Rep; 2024 Apr; 14(1):9275. PubMed ID: 38654130
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest.
    Wang X; Lin P; Ho JWK
    BMC Genomics; 2018 Jan; 19(Suppl 1):929. PubMed ID: 29363433
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.
    Karimzadeh M; Hoffman MM
    Genome Biol; 2022 Jun; 23(1):126. PubMed ID: 35681170
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data.
    Qin J; Hu Y; Yao JC; Leung RWT; Zhou Y; Qin Y; Wang J
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34374760
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Improved linking of motifs to their TFs using domain information.
    Baumgarten N; Schmidt F; Schulz MH
    Bioinformatics; 2020 Mar; 36(6):1655-1662. PubMed ID: 31742324
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Probing transcription factor combinatorics in different promoter classes and in enhancers.
    Vandel J; Cassan O; Lèbre S; Lecellier CH; Bréhélin L
    BMC Genomics; 2019 Feb; 20(1):103. PubMed ID: 30709337
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.