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M Borodovsky

Showing results (1-10 of 28) with videos related to

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Trends in Genetics : TIG|April 1, 1998
'Small genomics' on a large scale. E. coli and small genomes, American Society for Microbiology, Snowbird, UT, USA, 12-15 October 1997M Borodovsky
Nucleic Acids Research|September 11, 1999
Heuristic approach to deriving models for gene findingJ Besemer, M Borodovsky
Computers & Chemistry|September 1, 1994
Deriving non-homogeneous DNA Markov chain models by cluster analysis algorithm minimizing multiple alignment entropyM Borodovsky, A Peresetsky
Computer Applications in the Biosciences : CABIOS|October 1, 1992
First and second moment of counts of words in random texts generated by Markov chainsJ Kleffe, M Borodovsky
Bio Systems|January 1, 1993
Recognition of genes in DNA sequence with ambiguitiesM Borodovsky, J McIninch
Genome Research|December 10, 1998
How to interpret an anonymous bacterial genome: machine learning approach to gene identificationW S Hayes, M Borodovsky
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|August 11, 1998
Deriving ribosomal binding site (RBS) statistical models from unannotated DNA sequences and the use of the RBS model for N-terminal predictionW S Hayes, M Borodovsky
Nucleic Acids Research|March 21, 1998
GeneMark.hmm: new solutions for gene findingA V Lukashin, M Borodovsky
Computers & Chemistry|March 1, 1996
Statistical analysis of GeneMark performance by cross-validationJ Kleffe, K Hermann, M Borodovsky
Nucleic Acids Research|June 19, 2001
GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regionsJ Besemer, A Lomsadze, M Borodovsky
Pageof 3

Showing results (1-10 of 28) with videos related to

Sort By:
Pageof 3
Trends in Genetics : TIG|April 1, 1998
'Small genomics' on a large scale. E. coli and small genomes, American Society for Microbiology, Snowbird, UT, USA, 12-15 October 1997M Borodovsky
Nucleic Acids Research|September 11, 1999
Heuristic approach to deriving models for gene findingJ Besemer, M Borodovsky
Computers & Chemistry|September 1, 1994
Deriving non-homogeneous DNA Markov chain models by cluster analysis algorithm minimizing multiple alignment entropyM Borodovsky, A Peresetsky
Computer Applications in the Biosciences : CABIOS|October 1, 1992
First and second moment of counts of words in random texts generated by Markov chainsJ Kleffe, M Borodovsky
Bio Systems|January 1, 1993
Recognition of genes in DNA sequence with ambiguitiesM Borodovsky, J McIninch
Genome Research|December 10, 1998
How to interpret an anonymous bacterial genome: machine learning approach to gene identificationW S Hayes, M Borodovsky
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|August 11, 1998
Deriving ribosomal binding site (RBS) statistical models from unannotated DNA sequences and the use of the RBS model for N-terminal predictionW S Hayes, M Borodovsky
Nucleic Acids Research|March 21, 1998
GeneMark.hmm: new solutions for gene findingA V Lukashin, M Borodovsky
Computers & Chemistry|March 1, 1996
Statistical analysis of GeneMark performance by cross-validationJ Kleffe, K Hermann, M Borodovsky
Nucleic Acids Research|June 19, 2001
GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regionsJ Besemer, A Lomsadze, M Borodovsky
Pageof 3