BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

378 related articles for article (PubMed ID: 26634919)

  • 1. Progress and challenges in bioinformatics approaches for enhancer identification.
    Kleftogiannis D; Kalnis P; Bajic VB
    Brief Bioinform; 2016 Nov; 17(6):967-979. PubMed ID: 26634919
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome.
    Phan LT; Oh C; He T; Manavalan B
    Proteomics; 2023 Jul; 23(13-14):e2200409. PubMed ID: 37021401
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Enhancer prediction with histone modification marks using a hybrid neural network model.
    Lim A; Lim S; Kim S
    Methods; 2019 Aug; 166():48-56. PubMed ID: 30905748
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.
    Kim SG; Theera-Ampornpunt N; Fang CH; Harwani M; Grama A; Chaterji S
    BMC Syst Biol; 2016 Aug; 10 Suppl 2(Suppl 2):54. PubMed ID: 27490187
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.
    Basith S; Hasan MM; Lee G; Wei L; Manavalan B
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34226917
    [TBL] [Abstract][Full Text] [Related]  

  • 6. DEEP: a general computational framework for predicting enhancers.
    Kleftogiannis D; Kalnis P; Bajic VB
    Nucleic Acids Res; 2015 Jan; 43(1):e6. PubMed ID: 25378307
    [TBL] [Abstract][Full Text] [Related]  

  • 7. RFECS: a random-forest based algorithm for enhancer identification from chromatin state.
    Rajagopal N; Xie W; Li Y; Wagner U; Wang W; Stamatoyannopoulos J; Ernst J; Kellis M; Ren B
    PLoS Comput Biol; 2013; 9(3):e1002968. PubMed ID: 23526891
    [TBL] [Abstract][Full Text] [Related]  

  • 8. In the loop: promoter-enhancer interactions and bioinformatics.
    Mora A; Sandve GK; Gabrielsen OS; Eskeland R
    Brief Bioinform; 2016 Nov; 17(6):980-995. PubMed ID: 26586731
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Sequence based prediction of enhancer regions from DNA random walk.
    Singh AP; Mishra S; Jabin S
    Sci Rep; 2018 Oct; 8(1):15912. PubMed ID: 30374023
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Computational Approaches for Mining GRO-Seq Data to Identify and Characterize Active Enhancers.
    Nagari A; Murakami S; Malladi VS; Kraus WL
    Methods Mol Biol; 2017; 1468():121-38. PubMed ID: 27662874
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data.
    Podsiadło A; Wrzesień M; Paja W; Rudnicki W; Wilczyński B
    BMC Syst Biol; 2013; 7 Suppl 6(Suppl 6):S16. PubMed ID: 24565409
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Integrating diverse datasets improves developmental enhancer prediction.
    Erwin GD; Oksenberg N; Truty RM; Kostka D; Murphy KK; Ahituv N; Pollard KS; Capra JA
    PLoS Comput Biol; 2014 Jun; 10(6):e1003677. PubMed ID: 24967590
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Extrapolating histone marks across developmental stages, tissues, and species: an enhancer prediction case study.
    Capra JA
    BMC Genomics; 2015 Feb; 16(1):104. PubMed ID: 25765133
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Improved regulatory element prediction based on tissue-specific local epigenomic signatures.
    He Y; Gorkin DU; Dickel DE; Nery JR; Castanon RG; Lee AY; Shen Y; Visel A; Pennacchio LA; Ren B; Ecker JR
    Proc Natl Acad Sci U S A; 2017 Feb; 114(9):E1633-E1640. PubMed ID: 28193886
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Genome-Wide Prediction and Validation of Intergenic Enhancers in Arabidopsis Using Open Chromatin Signatures.
    Zhu B; Zhang W; Zhang T; Liu B; Jiang J
    Plant Cell; 2015 Sep; 27(9):2415-26. PubMed ID: 26373455
    [TBL] [Abstract][Full Text] [Related]  

  • 16. DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications.
    Lu Y; Qu W; Shan G; Zhang C
    PLoS One; 2015; 10(6):e0130622. PubMed ID: 26091399
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Taking promoters out of enhancers in sequence based predictions of tissue-specific mammalian enhancers.
    Herman-Izycka J; Wlasnowolski M; Wilczynski B
    BMC Med Genomics; 2017 May; 10(Suppl 1):34. PubMed ID: 28589862
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computational schemes for the prediction and annotation of enhancers from epigenomic assays.
    Whitaker JW; Nguyen TT; Zhu Y; Wildberg A; Wang W
    Methods; 2015 Jan; 72():86-94. PubMed ID: 25461775
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Non-coding transcription at cis-regulatory elements: computational and experimental approaches.
    Simonatto M; Barozzi I; Natoli G
    Methods; 2013 Sep; 63(1):66-75. PubMed ID: 23542771
    [TBL] [Abstract][Full Text] [Related]  

  • 20. EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species.
    Gao T; Qian J
    Nucleic Acids Res; 2020 Jan; 48(D1):D58-D64. PubMed ID: 31740966
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.