BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

255 related articles for article (PubMed ID: 29617928)

  • 1. DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.
    Wang M; Tai C; E W; Wei L
    Nucleic Acids Res; 2018 Jun; 46(11):e69. PubMed ID: 29617928
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.
    Han D; Li Y; Wang L; Liang X; Miao Y; Li W; Wang S; Wang Z
    Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38517697
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.
    Park S; Koh Y; Jeon H; Kim H; Yeo Y; Kang J
    Sci Rep; 2020 Aug; 10(1):13413. PubMed ID: 32770026
    [TBL] [Abstract][Full Text] [Related]  

  • 4. The functional consequences of variation in transcription factor binding.
    Cusanovich DA; Pavlovic B; Pritchard JK; Gilad Y
    PLoS Genet; 2014 Mar; 10(3):e1004226. PubMed ID: 24603674
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Quantifying Intensities of Transcription Factor-DNA Binding by Learning From an Ensemble of Protein Binding Microarrays.
    Quan L; Mei J; He R; Sun X; Nie L; Li K; Lyu Q
    IEEE J Biomed Health Inform; 2021 Jul; 25(7):2811-2819. PubMed ID: 33571101
    [TBL] [Abstract][Full Text] [Related]  

  • 6. De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets.
    Niu M; Tabari ES; Su Z
    BMC Genomics; 2014 Dec; 15():1047. PubMed ID: 25442502
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
    Zeng H; Hashimoto T; Kang DD; Gifford DK
    Bioinformatics; 2016 Feb; 32(4):490-6. PubMed ID: 26476779
    [TBL] [Abstract][Full Text] [Related]  

  • 9. PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants.
    Chow CN; Lee TY; Hung YC; Li GZ; Tseng KC; Liu YH; Kuo PL; Zheng HQ; Chang WC
    Nucleic Acids Res; 2019 Jan; 47(D1):D1155-D1163. PubMed ID: 30395277
    [TBL] [Abstract][Full Text] [Related]  

  • 10. DeepTFactor: A deep learning-based tool for the prediction of transcription factors.
    Kim GB; Gao Y; Palsson BO; Lee SY
    Proc Natl Acad Sci U S A; 2021 Jan; 118(2):. PubMed ID: 33372147
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.
    Wu WS; Lai FJ
    BMC Genomics; 2015; 16 Suppl 12(Suppl 12):S10. PubMed ID: 26679776
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
    Chen X; Neuwald AF; Hilakivi-Clarke L; Clarke R; Xuan J
    PLoS Comput Biol; 2021 Jul; 17(7):e1009203. PubMed ID: 34292930
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Evaluation of deep learning approaches for modeling transcription factor sequence specificity.
    Zhang Y; Mo Q; Xue L; Luo J
    Genomics; 2021 Nov; 113(6):3774-3781. PubMed ID: 34534646
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response.
    Xie X; Hanson C; Sinha S
    BMC Biol; 2019 Jul; 17(1):62. PubMed ID: 31362726
    [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. Parsing regulatory DNA: general tasks, techniques, and the PhyloGibbs approach.
    Siddharthan R
    J Biosci; 2007 Aug; 32(5):863-70. PubMed ID: 17914228
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Chromatin interaction-aware gene regulatory modeling with graph attention networks.
    Karbalayghareh A; Sahin M; Leslie CS
    Genome Res; 2022 May; 32(5):930-944. PubMed ID: 35396274
    [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. QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants.
    Martin V; Zhao J; Afek A; Mielko Z; Gordân R
    Nucleic Acids Res; 2019 Jul; 47(W1):W127-W135. PubMed ID: 31114870
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Disease-associated non-coding variants alter NKX2-5 DNA-binding affinity.
    Peña-Martínez EG; Rivera-Madera A; Pomales-Matos DA; Sanabria-Alberto L; Rosario-Cañuelas BM; Rodríguez-Ríos JM; Carrasquillo-Dones EA; Rodríguez-Martínez JA
    Biochim Biophys Acta Gene Regul Mech; 2023 Mar; 1866(1):194906. PubMed ID: 36690178
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.