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Items: 1 to 20 of 102

1.

An efficient algorithm for upper bound on the partition function of nucleic acids.

Chitsaz H, Forouzmand E, Haffari G.

J Comput Biol. 2013 Jul;20(7):486-94. doi: 10.1089/cmb.2013.0003.

PMID:
23829650
2.

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.

3.

Partition function and base pairing probabilities for RNA-RNA interaction prediction.

Huang FW, Qin J, Reidys CM, Stadler PF.

Bioinformatics. 2009 Oct 15;25(20):2646-54. doi: 10.1093/bioinformatics/btp481.

4.

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.

5.
6.

A partition function algorithm for interacting nucleic acid strands.

Chitsaz H, Salari R, Sahinalp SC, Backofen R.

Bioinformatics. 2009 Jun 15;25(12):i365-73. doi: 10.1093/bioinformatics/btp212.

7.

A statistical sampling algorithm for RNA secondary structure prediction.

Ding Y, Lawrence CE.

Nucleic Acids Res. 2003 Dec 15;31(24):7280-301.

8.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

Will S, Jabbari H.

Algorithms Mol Biol. 2016 Apr 23;11:7. doi: 10.1186/s13015-016-0071-y. Review.

9.

An algorithm for computing nucleic acid base-pairing probabilities including pseudoknots.

Dirks RM, Pierce NA.

J Comput Chem. 2004 Jul 30;25(10):1295-304.

PMID:
15139042
10.

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.

PMID:
22135038
11.

Efficient algorithms for probing the RNA mutation landscape.

Waldispühl J, Devadas S, Berger B, Clote P.

PLoS Comput Biol. 2008 Aug 8;4(8):e1000124. doi: 10.1371/journal.pcbi.1000124.

12.

Practicality and time complexity of a sparsified RNA folding algorithm.

Dimitrieva S, Bucher P.

J Bioinform Comput Biol. 2012 Apr;10(2):1241007. doi: 10.1142/S0219720012410077.

PMID:
22809342
13.

Fast evaluation of internal loops in RNA secondary structure prediction.

Lyngsø RB, Zuker M, Pedersen CN.

Bioinformatics. 1999 Jun;15(6):440-5.

14.

The RNA Newton polytope and learnability of energy parameters.

Forouzmand E, Chitsaz H.

Bioinformatics. 2013 Jul 1;29(13):i300-7. doi: 10.1093/bioinformatics/btt226.

15.

Energy-directed RNA structure prediction.

Hofacker IL.

Methods Mol Biol. 2014;1097:71-84. doi: 10.1007/978-1-62703-709-9_4. Review.

PMID:
24639155
16.
17.

An O(n(5)) algorithm for MFE prediction of kissing hairpins and 4-chains in nucleic acids.

Chen HL, Condon A, Jabbari H.

J Comput Biol. 2009 Jun;16(6):803-15. doi: 10.1089/cmb.2008.0219.

PMID:
19522664
18.

Computing the probability of RNA hairpin and multiloop formation.

Ding Y, Lorenz WA, Dotu I, Senter E, Clote P.

J Comput Biol. 2014 Mar;21(3):201-18. doi: 10.1089/cmb.2013.0148.

19.

A dynamic programming algorithm for RNA structure prediction including pseudoknots.

Rivas E, Eddy SR.

J Mol Biol. 1999 Feb 5;285(5):2053-68.

PMID:
9925784
20.

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.

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