| pubmed-article:16188929 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C0524637 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C1511790 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C0439851 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C0332462 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C1881303 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C1552596 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C1947931 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C0205157 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C1334043 | lld:lifeskim |
| pubmed-article:16188929 | lifeskim:mentions | umls-concept:C2697616 | lld:lifeskim |
| pubmed-article:16188929 | pubmed:issue | 23 | lld:pubmed |
| pubmed-article:16188929 | pubmed:dateCreated | 2005-11-23 | lld:pubmed |
| pubmed-article:16188929 | pubmed:abstractText | Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them. | lld:pubmed |
| pubmed-article:16188929 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:16188929 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:16188929 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16188929 | pubmed:month | Dec | lld:pubmed |
| pubmed-article:16188929 | pubmed:issn | 1367-4803 | lld:pubmed |
| pubmed-article:16188929 | pubmed:author | pubmed-author:KarypisGeorge... | lld:pubmed |
| pubmed-article:16188929 | pubmed:author | pubmed-author:RangwalaHuzef... | lld:pubmed |
| pubmed-article:16188929 | pubmed:issnType | Print | lld:pubmed |
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| pubmed-article:16188929 | pubmed:volume | 21 | lld:pubmed |
| pubmed-article:16188929 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16188929 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16188929 | pubmed:pagination | 4239-47 | lld:pubmed |
| pubmed-article:16188929 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
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| pubmed-article:16188929 | pubmed:year | 2005 | lld:pubmed |
| pubmed-article:16188929 | pubmed:articleTitle | Profile-based direct kernels for remote homology detection and fold recognition. | lld:pubmed |
| pubmed-article:16188929 | pubmed:affiliation | Department of Computer Science and Engineering, University of Minnesota Minneapolis, MN 55455, USA. | lld:pubmed |
| pubmed-article:16188929 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16188929 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:16188929 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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