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Items: 1 to 20 of 89

1.

Reducing the worst case running times of a family of RNA and CFG problems, using Valiant's approach.

Zakov S, Tsur D, Ziv-Ukelson M.

Algorithms Mol Biol. 2011 Aug 18;6(1):20. doi: 10.1186/1748-7188-6-20.

2.

Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures.

Scheid A, Nebel ME.

BMC Bioinformatics. 2012 Jul 9;13:159. doi: 10.1186/1471-2105-13-159.

3.

Evaluation of a sophisticated SCFG design for RNA secondary structure prediction.

Nebel ME, Scheid A.

Theory Biosci. 2011 Dec;130(4):313-36. doi: 10.1007/s12064-011-0139-7. Epub 2011 Dec 2.

PMID:
22135038
4.

Sparsification of RNA structure prediction including pseudoknots.

Möhl M, Salari R, Will S, Backofen R, Sahinalp SC.

Algorithms Mol Biol. 2010 Dec 31;5:39. doi: 10.1186/1748-7188-5-39.

5.

An algebraic geometry approach to protein structure determination from NMR data.

Wang L, Mettu RR, Donald BR.

Proc IEEE Comput Syst Bioinform Conf. 2005:235-46.

PMID:
16447981
6.

Multithreaded parsing for predicting RNA secondary structures.

Al-Mulhem MS.

Int J Bioinform Res Appl. 2010;6(6):609-21.

PMID:
21354966
7.

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures.

Matsui H, Sato K, Sakakibara Y.

Proc IEEE Comput Syst Bioinform Conf. 2004:290-9.

PMID:
16448022
8.

TurboFold: iterative probabilistic estimation of secondary structures for multiple RNA sequences.

Harmanci AO, Sharma G, Mathews DH.

BMC Bioinformatics. 2011 Apr 20;12:108. doi: 10.1186/1471-2105-12-108.

9.

Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars.

Sükösd Z, Knudsen B, Vaerum M, Kjems J, Andersen ES.

BMC Bioinformatics. 2011 Apr 18;12:103. doi: 10.1186/1471-2105-12-103.

10.

A simple, practical and complete O(n3/log n)-time algorithm for RNA folding using the Four-Russians speedup.

Frid Y, Gusfield D.

Algorithms Mol Biol. 2010 Jan 4;5:13. doi: 10.1186/1748-7188-5-13.

11.

Stochastic modeling of RNA pseudoknotted structures: a grammatical approach.

Cai L, Malmberg RL, Wu Y.

Bioinformatics. 2003;19 Suppl 1:i66-73.

12.

Rnall: an efficient algorithm for predicting RNA local secondary structural landscape in genomes.

Wan XF, Lin G, Xu D.

J Bioinform Comput Biol. 2006 Oct;4(5):1015-31.

PMID:
17099939
13.

Analysis of energy-based algorithms for RNA secondary structure prediction.

Hajiaghayi M, Condon A, Hoos HH.

BMC Bioinformatics. 2012 Feb 1;13:22. doi: 10.1186/1471-2105-13-22.

14.

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures.

Matsui H, Sato K, Sakakibara Y.

Bioinformatics. 2005 Jun 1;21(11):2611-7. Epub 2005 Mar 22.

15.

Partition function and base pairing probabilities of RNA heterodimers.

Bernhart SH, Tafer H, Mückstein U, Flamm C, Stadler PF, Hofacker IL.

Algorithms Mol Biol. 2006 Mar 16;1(1):3.

16.

Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure.

Reinharz V, Major F, Waldispühl J.

Bioinformatics. 2012 Jun 15;28(12):i207-14. doi: 10.1093/bioinformatics/bts226.

17.

A stochastic context free grammar based framework for analysis of protein sequences.

Dyrka W, Nebel JC.

BMC Bioinformatics. 2009 Oct 8;10:323. doi: 10.1186/1471-2105-10-323.

18.
19.

Random generation of RNA secondary structures according to native distributions.

Nebel ME, Scheid A, Weinberg F.

Algorithms Mol Biol. 2011 Oct 12;6:24. doi: 10.1186/1748-7188-6-24.

20.

Computational RNA secondary structure design: empirical complexity and improved methods.

Aguirre-Hernández R, Hoos HH, Condon A.

BMC Bioinformatics. 2007 Jan 31;8:34.

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