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 *

181 related articles for article (PubMed ID: 34996354)

  • 1. Computational modeling of chromatin accessibility identified important epigenomic regulators.
    Zhao Y; Dong Y; Hong W; Jiang C; Yao K; Cheng C
    BMC Genomics; 2022 Jan; 23(1):19. PubMed ID: 34996354
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Relationship between histone modifications and transcription factor binding is protein family specific.
    Xin B; Rohs R
    Genome Res; 2018 Mar; 28(3):321-333. PubMed ID: 29326300
    [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. Revealing transcription factor and histone modification co-localization and dynamics across cell lines by integrating ChIP-seq and RNA-seq data.
    Zhang L; Xue G; Liu J; Li Q; Wang Y
    BMC Genomics; 2018 Dec; 19(Suppl 10):914. PubMed ID: 30598100
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Sequence and chromatin determinants of cell-type-specific transcription factor binding.
    Arvey A; Agius P; Noble WS; Leslie C
    Genome Res; 2012 Sep; 22(9):1723-34. PubMed ID: 22955984
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.
    Kumar S; Bucher P
    BMC Bioinformatics; 2016 Jan; 17 Suppl 1(Suppl 1):4. PubMed ID: 26818008
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. Modeling co-occupancy of transcription factors using chromatin features.
    Liu L; Zhao W; Zhou X
    Nucleic Acids Res; 2016 Mar; 44(5):e49. PubMed ID: 26590261
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Role of chromatin and transcriptional co-regulators in mediating p63-genome interactions in keratinocytes.
    Sethi I; Sinha S; Buck MJ
    BMC Genomics; 2014 Nov; 15(1):1042. PubMed ID: 25433490
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Genome-wide in silico prediction of gene expression.
    McLeay RC; Lesluyes T; Cuellar Partida G; Bailey TL
    Bioinformatics; 2012 Nov; 28(21):2789-96. PubMed ID: 22954627
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Integrative prediction of gene expression with chromatin accessibility and conformation data.
    Schmidt F; Kern F; Schulz MH
    Epigenetics Chromatin; 2020 Feb; 13(1):4. PubMed ID: 32029002
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification of mammalian transcription factors that bind to inaccessible chromatin.
    Pop RT; Pisante A; Nagy D; Martin PCN; Mikheeva LA; Hayat A; Ficz G; Zabet NR
    Nucleic Acids Res; 2023 Sep; 51(16):8480-8495. PubMed ID: 37486787
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting expression: the complementary power of histone modification and transcription factor binding data.
    Budden DM; Hurley DG; Cursons J; Markham JF; Davis MJ; Crampin EJ
    Epigenetics Chromatin; 2014; 7(1):36. PubMed ID: 25489339
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Widespread contribution of transposable elements to the innovation of gene regulatory networks.
    Sundaram V; Cheng Y; Ma Z; Li D; Xing X; Edge P; Snyder MP; Wang T
    Genome Res; 2014 Dec; 24(12):1963-76. PubMed ID: 25319995
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.
    Schmidt F; Gasparoni N; Gasparoni G; Gianmoena K; Cadenas C; Polansky JK; Ebert P; Nordström K; Barann M; Sinha A; Fröhler S; Xiong J; Dehghani Amirabad A; Behjati Ardakani F; Hutter B; Zipprich G; Felder B; Eils J; Brors B; Chen W; Hengstler JG; Hamann A; Lengauer T; Rosenstiel P; Walter J; Schulz MH
    Nucleic Acids Res; 2017 Jan; 45(1):54-66. PubMed ID: 27899623
    [TBL] [Abstract][Full Text] [Related]  

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

  • 19. Anchor: trans-cell type prediction of transcription factor binding sites.
    Li H; Quang D; Guan Y
    Genome Res; 2019 Feb; 29(2):281-292. PubMed ID: 30567711
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DeepHistone: a deep learning approach to predicting histone modifications.
    Yin Q; Wu M; Liu Q; Lv H; Jiang R
    BMC Genomics; 2019 Apr; 20(Suppl 2):193. PubMed ID: 30967126
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.