|
rdf:type |
|
|
lifeskim:mentions |
|
|
pubmed:dateCreated |
1999-8-10
|
|
pubmed:abstractText |
Hidden Markov Models (HMMs) provide a flexible method for representing protein sequence data. Highly divergent data require a more complex approach to HMM generation than previously demonstrated. We describe a strategy of motif anchoring and sub-class modeling that aids in the construction of more informative HMMs as determined by a new algorithm called a stability measure.
|
|
pubmed:grant |
|
|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:chemical |
|
|
pubmed:status |
MEDLINE
|
|
pubmed:issn |
1793-5091
|
|
pubmed:author |
|
|
pubmed:issnType |
Print
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
162-70
|
|
pubmed:dateRevised |
2007-11-14
|
|
pubmed:meshHeading |
pubmed-meshheading:10380194-Algorithms,
pubmed-meshheading:10380194-Amino Acid Sequence,
pubmed-meshheading:10380194-Computational Biology,
pubmed-meshheading:10380194-Computer Simulation,
pubmed-meshheading:10380194-Databases, Factual,
pubmed-meshheading:10380194-Markov Chains,
pubmed-meshheading:10380194-Proteins,
pubmed-meshheading:10380194-Retroelements,
pubmed-meshheading:10380194-Sequence Alignment,
pubmed-meshheading:10380194-Sequence Homology, Amino Acid,
pubmed-meshheading:10380194-Software
|
|
pubmed:year |
1999
|
|
pubmed:articleTitle |
The effects of ordered-series-of-motifs anchoring and sub-class modeling on the generation of HMMs representing highly divergent protein sequences.
|
|
pubmed:affiliation |
Department of Biological Sciences, UNLV, Las Vegas, NV 89129, USA.
|
|
pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.
|
