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Items: 1 to 20 of 106

1.

A novel statistical measure for sequence comparison on the basis of k-word counts.

Yang X, Wang T.

J Theor Biol. 2013 Feb 7;318:91-100. doi: 10.1016/j.jtbi.2012.10.035. Epub 2012 Nov 9.

PMID:
23147229
2.

Numerical characteristics of word frequencies and their application to dissimilarity measure for sequence comparison.

Dai Q, Liu X, Yao Y, Zhao F.

J Theor Biol. 2011 May 7;276(1):174-80. doi: 10.1016/j.jtbi.2011.02.005. Epub 2011 Feb 18.

PMID:
21334347
3.

Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison.

Dai Q, Yang Y, Wang T.

Bioinformatics. 2008 Oct 15;24(20):2296-302. doi: 10.1093/bioinformatics/btn436. Epub 2008 Aug 18.

PMID:
18710871
4.

An efficient binomial model-based measure for sequence comparison and its application.

Liu X, Dai Q, Li L, He Z.

J Biomol Struct Dyn. 2011 Apr;28(5):833-43.

PMID:
21294594
5.

Using Gaussian model to improve biological sequence comparison.

Dai Q, Liu X, Li L, Yao Y, Han B, Zhu L.

J Comput Chem. 2010 Jan 30;31(2):351-61. doi: 10.1002/jcc.21322.

PMID:
19479732
6.

Comparison study on k-word statistical measures for protein: from sequence to 'sequence space'.

Dai Q, Wang T.

BMC Bioinformatics. 2008 Sep 23;9:394. doi: 10.1186/1471-2105-9-394.

7.

Alignment free comparison: similarity distribution between the DNA primary sequences based on the shortest absent word.

Yang L, Zhang X, Zhu H.

J Theor Biol. 2012 Feb 21;295:125-31. doi: 10.1016/j.jtbi.2011.11.021. Epub 2011 Dec 1.

PMID:
22138094
8.

Optimal word sizes for dissimilarity measures and estimation of the degree of dissimilarity between DNA sequences.

Wu TJ, Huang YH, Li LA.

Bioinformatics. 2005 Nov 15;21(22):4125-32. Epub 2005 Sep 6.

PMID:
16144805
9.

Linear regression model of short k-word: a similarity distance suitable for biological sequences with various lengths.

Yang X, Wang T.

J Theor Biol. 2013 Nov 21;337:61-70. doi: 10.1016/j.jtbi.2013.07.028. Epub 2013 Aug 8.

PMID:
23933105
10.

Weighted relative entropy for alignment-free sequence comparison based on Markov model.

Chang G, Wang T.

J Biomol Struct Dyn. 2011 Feb;28(4):545-55.

PMID:
21142223
11.

A measure of DNA sequence dissimilarity based on free energy of nearest-neighbor interaction.

Zhang Y, Chen W.

J Biomol Struct Dyn. 2011 Feb;28(4):557-65.

PMID:
21142215
12.

A novel hierarchical clustering algorithm for gene sequences.

Wei D, Jiang Q, Wei Y, Wang S.

BMC Bioinformatics. 2012 Jul 23;13:174. doi: 10.1186/1471-2105-13-174.

13.

The Burrows-Wheeler similarity distribution between biological sequences based on Burrows-Wheeler transform.

Yang L, Zhang X, Wang T.

J Theor Biol. 2010 Feb 21;262(4):742-9. doi: 10.1016/j.jtbi.2009.10.033. Epub 2009 Nov 10.

PMID:
19903487
14.

Using Markov model to improve word normalization algorithm for biological sequence comparison.

Dai Q, Liu X, Yao Y, Zhao F.

Amino Acids. 2012 May;42(5):1867-77. doi: 10.1007/s00726-011-0906-2. Epub 2011 Apr 20.

PMID:
21505825
15.
16.

A simple k-word interval method for phylogenetic analysis of DNA sequences.

Ding S, Li Y, Yang X, Wang T.

J Theor Biol. 2013 Jan 21;317:192-9. doi: 10.1016/j.jtbi.2012.10.010. Epub 2012 Oct 18.

PMID:
23085256
17.

Sequence comparison alignment-free approach based on suffix tree and L-words frequency.

Soares I, Goios A, Amorim A.

ScientificWorldJournal. 2012;2012:450124. Epub 2012 Sep 10.

18.

CLUSS: clustering of protein sequences based on a new similarity measure.

Kelil A, Wang S, Brzezinski R, Fleury A.

BMC Bioinformatics. 2007 Aug 4;8:286.

19.

Genome BLAST distance phylogenies inferred from whole plastid and whole mitochondrion genome sequences.

Auch AF, Henz SR, Holland BR, Göker M.

BMC Bioinformatics. 2006 Jul 19;7:350.

20.

A new method to cluster DNA sequences using Fourier power spectrum.

Hoang T, Yin C, Zheng H, Yu C, Lucy He R, Yau SS.

J Theor Biol. 2015 May 7;372:135-45. doi: 10.1016/j.jtbi.2015.02.026. Epub 2015 Mar 5.

PMID:
25747773

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