| pubmed-article:17099939 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:17099939 | lifeskim:mentions | umls-concept:C0035668 | lld:lifeskim |
| pubmed-article:17099939 | lifeskim:mentions | umls-concept:C0017428 | lld:lifeskim |
| pubmed-article:17099939 | lifeskim:mentions | umls-concept:C0205276 | lld:lifeskim |
| pubmed-article:17099939 | lifeskim:mentions | umls-concept:C0002045 | lld:lifeskim |
| pubmed-article:17099939 | lifeskim:mentions | umls-concept:C0678594 | lld:lifeskim |
| pubmed-article:17099939 | lifeskim:mentions | umls-concept:C0175668 | lld:lifeskim |
| pubmed-article:17099939 | pubmed:issue | 5 | lld:pubmed |
| pubmed-article:17099939 | pubmed:dateCreated | 2006-11-13 | lld:pubmed |
| pubmed-article:17099939 | pubmed:abstractText | The information of RNA local secondary structures (LSSs) can help retrieve biologically important motifs and study functions of RNA molecules. Most of the current RNA secondary structure prediction tools are not suitable for RNA LSS prediction on the genome scale due to high computational complexity. | lld:pubmed |
| pubmed-article:17099939 | pubmed:language | eng | lld:pubmed |
| pubmed-article:17099939 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17099939 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:17099939 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17099939 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:17099939 | pubmed:month | Oct | lld:pubmed |
| pubmed-article:17099939 | pubmed:issn | 0219-7200 | lld:pubmed |
| pubmed-article:17099939 | pubmed:author | pubmed-author:DangC LCL | lld:pubmed |
| pubmed-article:17099939 | pubmed:author | pubmed-author:LinGuohuiG | lld:pubmed |
| pubmed-article:17099939 | pubmed:author | pubmed-author:WanXiu-FengXF | lld:pubmed |
| pubmed-article:17099939 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:17099939 | pubmed:volume | 4 | lld:pubmed |
| pubmed-article:17099939 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:17099939 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:17099939 | pubmed:pagination | 1015-31 | lld:pubmed |
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| pubmed-article:17099939 | pubmed:meshHeading | pubmed-meshheading:17099939... | lld:pubmed |
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| pubmed-article:17099939 | pubmed:meshHeading | pubmed-meshheading:17099939... | lld:pubmed |
| pubmed-article:17099939 | pubmed:meshHeading | pubmed-meshheading:17099939... | lld:pubmed |
| pubmed-article:17099939 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:17099939 | pubmed:articleTitle | Rnall: an efficient algorithm for predicting RNA local secondary structural landscape in genomes. | lld:pubmed |
| pubmed-article:17099939 | pubmed:affiliation | Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri - Columbia, Columbia, MO 65211, USA. wanx@muohio.edu | lld:pubmed |
| pubmed-article:17099939 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:17099939 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:17099939 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:17099939 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:17099939 | lld:pubmed |
