BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

775 related articles for article (PubMed ID: 28750606)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.
    Rautio S; Lähdesmäki H
    BMC Bioinformatics; 2015 Dec; 16():413. PubMed ID: 26703974
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 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. Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes.
    Afek A; Cohen H; Barber-Zucker S; Gordân R; Lukatsky DB
    PLoS Comput Biol; 2015 Aug; 11(8):e1004429. PubMed ID: 26285121
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Predicting transcription factor binding using ensemble random forest models.
    Behjati Ardakani F; Schmidt F; Schulz MH
    F1000Res; 2018; 7():1603. PubMed ID: 31723409
    [No Abstract]   [Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. Cell-type specificity of ChIP-predicted transcription factor binding sites.
    Håndstad T; Rye M; Močnik R; Drabløs F; Sætrom P
    BMC Genomics; 2012 Aug; 13():372. PubMed ID: 22863112
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
    Agius P; Arvey A; Chang W; Noble WS; Leslie C
    PLoS Comput Biol; 2010 Sep; 6(9):. PubMed ID: 20838582
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.
    Tahara S; Tsuchiya T; Matsumoto H; Ozaki H
    BMC Genomics; 2023 Oct; 24(1):597. PubMed ID: 37805453
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An improved ChIP-seq peak detection system for simultaneously identifying post-translational modified transcription factors by combinatorial fusion, using SUMOylation as an example.
    Cheng CY; Chu CH; Hsu HW; Hsu FR; Tang CY; Wang WC; Kung HJ; Chang PC
    BMC Genomics; 2014; 15 Suppl 1(Suppl 1):S1. PubMed ID: 24564277
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Base-resolution methylation patterns accurately predict transcription factor bindings in vivo.
    Xu T; Li B; Zhao M; Szulwach KE; Street RC; Lin L; Yao B; Zhang F; Jin P; Wu H; Qin ZS
    Nucleic Acids Res; 2015 Mar; 43(5):2757-66. PubMed ID: 25722376
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 39.