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Automated sequence homology : using empirical correlations to create graph-based networks for the elucidation of protein relationships.

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dc.contributor.advisor Baker, Erich J.
dc.contributor.author Bush, Stephen J.
dc.contributor.other Baylor University. Institute of Biomedical Studies. en
dc.date.copyright 2008-08
dc.identifier.uri http://hdl.handle.net/2104/5221
dc.description.abstract Identification of sequence homology has presented a formidable obstacle despite significant increases in both technological capability and detailed knowledge of genomes and proteomes. While PSI-BLAST remains the popular tool for the job, it often returns inaccurate results with unacceptable levels of false positives. In order to increase the sensitivity and accuracy of homology finding, we have developed a software application called Automated Sequence Homology that bypasses these shortcomings and provides reliable and precise results. The system presented here is based upon the creation of a graph-based network highlighting the relational connections between proteins using empirical correlations. It takes a step back from PSI-BLAST to the acclaimed BLAST algorithm to create a sampling of the protein relational network. en
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission. en
dc.subject Bioinformatics. en
dc.subject Application software -- Development. en
dc.subject Homology (Biology) en
dc.subject Proteins. en
dc.subject Blast (Electronic resource) en
dc.title Automated sequence homology : using empirical correlations to create graph-based networks for the elucidation of protein relationships. en
dc.type Thesis en
dc.description.degree B.S. en
dc.rights.accessrights Worldwide access en
dc.contributor.department Biomedical Studies. en


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