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.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

269 related items for PubMed ID: 24472686

  • 1. Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data.
    Levitsky VG, Kulakovskiy IV, Ershov NI, Oshchepkov DY, Makeev VJ, Hodgman TC, Merkulova TI.
    BMC Genomics; 2014 Jan 29; 15(1):80. PubMed ID: 24472686
    [Abstract] [Full Text] [Related]

  • 2. From binding motifs in ChIP-Seq data to improved models of transcription factor binding sites.
    Kulakovskiy I, Levitsky V, Oshchepkov D, Bryzgalov L, Vorontsov I, Makeev V.
    J Bioinform Comput Biol; 2013 Feb 29; 11(1):1340004. PubMed ID: 23427986
    [Abstract] [Full Text] [Related]

  • 3. Hidden heterogeneity of transcription factor binding sites: A case study of SF-1.
    Levitsky VG, Oshchepkov DY, Klimova NV, Ignatieva EV, Vasiliev GV, Merkulov VM, Merkulova TI.
    Comput Biol Chem; 2016 Oct 29; 64():19-32. PubMed ID: 27235721
    [Abstract] [Full Text] [Related]

  • 4. Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.
    Worsley Hunt R, Mathelier A, Del Peso L, Wasserman WW.
    BMC Genomics; 2014 Jun 13; 15(1):472. PubMed ID: 24927817
    [Abstract] [Full Text] [Related]

  • 5. Application of alternative de novo motif recognition models for analysis of structural heterogeneity of transcription factor binding sites: a case study of FOXA2 binding sites.
    Tsukanov AV, Levitsky VG, Merkulova TI.
    Vavilovskii Zhurnal Genet Selektsii; 2021 Feb 13; 25(1):7. PubMed ID: 34547062
    [Abstract] [Full Text] [Related]

  • 6. Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis.
    Tsukanov AV, Mironova VV, Levitsky VG.
    Front Plant Sci; 2022 Feb 13; 13():938545. PubMed ID: 35968123
    [Abstract] [Full Text] [Related]

  • 7. The next generation of transcription factor binding site prediction.
    Mathelier A, Wasserman WW.
    PLoS Comput Biol; 2013 Feb 13; 9(9):e1003214. PubMed ID: 24039567
    [Abstract] [Full Text] [Related]

  • 8. Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome.
    Kuznetsov VA, Singh O, Jenjaroenpun P.
    BMC Genomics; 2010 Feb 10; 11 Suppl 1(Suppl 1):S12. PubMed ID: 20158869
    [Abstract] [Full Text] [Related]

  • 9. Tree-based position weight matrix approach to model transcription factor binding site profiles.
    Bi Y, Kim H, Gupta R, Davuluri RV.
    PLoS One; 2011 Feb 10; 6(9):e24210. PubMed ID: 21912677
    [Abstract] [Full Text] [Related]

  • 10. 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 Feb 10; 15 Suppl 1(Suppl 1):S1. PubMed ID: 24564277
    [Abstract] [Full Text] [Related]

  • 11. Optimally choosing PWM motif databases and sequence scanning approaches based on ChIP-seq data.
    Dabrowski M, Dojer N, Krystkowiak I, Kaminska B, Wilczynski B.
    BMC Bioinformatics; 2015 May 01; 16():140. PubMed ID: 25927199
    [Abstract] [Full Text] [Related]

  • 12. Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite.
    Wang X, Zhang X.
    BMC Syst Biol; 2011 May 01; 5 Suppl 2(Suppl 2):S3. PubMed ID: 22784574
    [Abstract] [Full Text] [Related]

  • 13. Discovering unknown human and mouse transcription factor binding sites and their characteristics from ChIP-seq data.
    Yu CP, Kuo CH, Nelson CW, Chen CA, Soh ZT, Lin JJ, Hsiao RX, Chang CY, Li WH.
    Proc Natl Acad Sci U S A; 2021 May 18; 118(20):. PubMed ID: 33975951
    [Abstract] [Full Text] [Related]

  • 14. Evaluating tools for transcription factor binding site prediction.
    Jayaram N, Usvyat D, R Martin AC.
    BMC Bioinformatics; 2016 Nov 02; 17(1):547. PubMed ID: 27806697
    [Abstract] [Full Text] [Related]

  • 15. Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions.
    Levitsky VG, Ignatieva EV, Ananko EA, Turnaev II, Merkulova TI, Kolchanov NA, Hodgman TC.
    BMC Bioinformatics; 2007 Dec 19; 8():481. PubMed ID: 18093302
    [Abstract] [Full Text] [Related]

  • 16. 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 01; 4(12):e8155. PubMed ID: 19956545
    [Abstract] [Full Text] [Related]

  • 17. An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data.
    Liu B, Yang J, Li Y, McDermaid A, Ma Q.
    Brief Bioinform; 2018 Sep 28; 19(5):1069-1081. PubMed ID: 28334268
    [Abstract] [Full Text] [Related]

  • 18. A novel method for improved accuracy of transcription factor binding site prediction.
    Khamis AM, Motwalli O, Oliva R, Jankovic BR, Medvedeva YA, Ashoor H, Essack M, Gao X, Bajic VB.
    Nucleic Acids Res; 2018 Jul 06; 46(12):e72. PubMed ID: 29617876
    [Abstract] [Full Text] [Related]

  • 19. Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets.
    Worsley Hunt R, Wasserman WW.
    Genome Biol; 2014 Jul 29; 15(7):412. PubMed ID: 25070602
    [Abstract] [Full Text] [Related]

  • 20. ChIPulate: A comprehensive ChIP-seq simulation pipeline.
    Datta V, Hannenhalli S, Siddharthan R.
    PLoS Comput Biol; 2019 Mar 29; 15(3):e1006921. PubMed ID: 30897079
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 14.