| pubmed-article:10380194 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C0019453 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C1280500 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C0002518 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C1624581 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C0870071 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C0205250 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C0079411 | lld:lifeskim |
| pubmed-article:10380194 | lifeskim:mentions | umls-concept:C1293132 | lld:lifeskim |
| pubmed-article:10380194 | pubmed:dateCreated | 1999-8-10 | lld:pubmed |
| pubmed-article:10380194 | 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. | lld:pubmed |
| pubmed-article:10380194 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10380194 | pubmed:language | eng | lld:pubmed |
| pubmed-article:10380194 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10380194 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:10380194 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10380194 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10380194 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:10380194 | pubmed:issn | 1793-5091 | lld:pubmed |
| pubmed-article:10380194 | pubmed:author | pubmed-author:KowalskiJJ | lld:pubmed |
| pubmed-article:10380194 | pubmed:author | pubmed-author:McclureM AMA | lld:pubmed |
| pubmed-article:10380194 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:10380194 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:10380194 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:10380194 | pubmed:pagination | 162-70 | lld:pubmed |
| pubmed-article:10380194 | pubmed:dateRevised | 2007-11-14 | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:meshHeading | pubmed-meshheading:10380194... | lld:pubmed |
| pubmed-article:10380194 | pubmed:year | 1999 | lld:pubmed |
| pubmed-article:10380194 | pubmed:articleTitle | The effects of ordered-series-of-motifs anchoring and sub-class modeling on the generation of HMMs representing highly divergent protein sequences. | lld:pubmed |
| pubmed-article:10380194 | pubmed:affiliation | Department of Biological Sciences, UNLV, Las Vegas, NV 89129, USA. | lld:pubmed |
| pubmed-article:10380194 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:10380194 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
