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Items: 1 to 20 of 173

1.

Metamotifs--a generative model for building families of nucleotide position weight matrices.

Piipari M, Down TA, Hubbard TJ.

BMC Bioinformatics. 2010 Jun 25;11:348. doi: 10.1186/1471-2105-11-348.

2.

Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data.

Eggeling R, Roos T, Myllymäki P, Grosse I.

BMC Bioinformatics. 2015 Nov 9;16:375. doi: 10.1186/s12859-015-0797-4.

3.
4.

A novel Bayesian DNA motif comparison method for clustering and retrieval.

Habib N, Kaplan T, Margalit H, Friedman N.

PLoS Comput Biol. 2008 Feb 29;4(2):e1000010. doi: 10.1371/journal.pcbi.1000010. Erratum in: PLoS Comput Biol. 2011 May;7(5). doiL10.1371/annotation/d876137b-59c5-48cf-8491-c8cf12f26a9b.

5.

Improved benchmarks for computational motif discovery.

Sandve GK, Abul O, Walseng V, Drabløs F.

BMC Bioinformatics. 2007 Jun 8;8:193.

6.

Alignment-free clustering of transcription factor binding motifs using a genetic-k-medoids approach.

Broin PÓ, Smith TJ, Golden AA.

BMC Bioinformatics. 2015 Jan 28;16:22. doi: 10.1186/s12859-015-0450-2.

7.

Increasing coverage of transcription factor position weight matrices through domain-level homology.

Bernard B, Thorsson V, Rovira H, Shmulevich I.

PLoS One. 2012;7(8):e42779. doi: 10.1371/journal.pone.0042779. Epub 2012 Aug 27.

8.

BayesMotif: de novo protein sorting motif discovery from impure datasets.

Hu J, Zhang F.

BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S66. doi: 10.1186/1471-2105-11-S1-S66.

9.

Combining sequence and time series expression data to learn transcriptional modules.

Kundaje A, Middendorf M, Gao F, Wiggins C, Leslie C.

IEEE/ACM Trans Comput Biol Bioinform. 2005 Jul-Sep;2(3):194-202.

PMID:
17044183
10.

P-value-based regulatory motif discovery using positional weight matrices.

Hartmann H, Guthöhrlein EW, Siebert M, Luehr S, Söding J.

Genome Res. 2013 Jan;23(1):181-94. doi: 10.1101/gr.139881.112. Epub 2012 Sep 18.

11.

Probabilistic models for semisupervised discriminative motif discovery in DNA sequences.

Kim JK, Choi S.

IEEE/ACM Trans Comput Biol Bioinform. 2011 Sep-Oct;8(5):1309-17. doi: 10.1109/TCBB.2010.84.

PMID:
21778525
12.
13.

A novel ensemble learning method for de novo computational identification of DNA binding sites.

Chakravarty A, Carlson JM, Khetani RS, Gross RH.

BMC Bioinformatics. 2007 Jul 12;8:249.

14.

Poly(A) motif prediction using spectral latent features from human DNA sequences.

Xie B, Jankovic BR, Bajic VB, Song L, Gao X.

Bioinformatics. 2013 Jul 1;29(13):i316-25. doi: 10.1093/bioinformatics/btt218.

15.

A discriminative approach for unsupervised clustering of DNA sequence motifs.

Stegmaier P, Kel A, Wingender E, Borlak J.

PLoS Comput Biol. 2013;9(3):e1002958. doi: 10.1371/journal.pcbi.1002958. Epub 2013 Mar 21.

16.

Reliable scaling of position weight matrices for binding strength comparisons between transcription factors.

Ma X, Ezer D, Navarro C, Adryan B.

BMC Bioinformatics. 2015 Aug 20;16:265. doi: 10.1186/s12859-015-0666-1.

17.

Learning position weight matrices from sequence and expression data.

Chen X, Guo L, Fan Z, Jiang T.

Comput Syst Bioinformatics Conf. 2007;6:249-60.

18.

Modeling within-motif dependence for transcription factor binding site predictions.

Zhou Q, Liu JS.

Bioinformatics. 2004 Apr 12;20(6):909-16. Epub 2004 Jan 29.

PMID:
14751969
19.

NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence.

Down TA, Hubbard TJ.

Nucleic Acids Res. 2005 Mar 10;33(5):1445-53. Print 2005.

20.

Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.

Zhang Z, Chang CW, Hugo W, Cheung E, Sung WK.

J Comput Biol. 2013 Mar;20(3):237-48. doi: 10.1089/cmb.2012.0233.

PMID:
23461573

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