| pubmed-article:17553836 | rdf:type | pubmed:Citation | lld:pubmed |
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| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C0026336 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C1101610 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C0008902 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C0439605 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C0205195 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C0205237 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C2348519 | lld:lifeskim |
| pubmed-article:17553836 | lifeskim:mentions | umls-concept:C1709634 | lld:lifeskim |
| pubmed-article:17553836 | pubmed:issue | Web Server issue | lld:pubmed |
| pubmed-article:17553836 | pubmed:dateCreated | 2007-7-16 | lld:pubmed |
| pubmed-article:17553836 | pubmed:abstractText | To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one. | lld:pubmed |
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| pubmed-article:17553836 | pubmed:language | eng | lld:pubmed |
| pubmed-article:17553836 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17553836 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:17553836 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:17553836 | pubmed:month | Jul | lld:pubmed |
| pubmed-article:17553836 | pubmed:issn | 1362-4962 | lld:pubmed |
| pubmed-article:17553836 | pubmed:author | pubmed-author:YewDD | lld:pubmed |
| pubmed-article:17553836 | pubmed:author | pubmed-author:LuZuhongZ | lld:pubmed |
| pubmed-article:17553836 | pubmed:author | pubmed-author:SunXiaoX | lld:pubmed |
| pubmed-article:17553836 | pubmed:author | pubmed-author:JiangPengP | lld:pubmed |
| pubmed-article:17553836 | pubmed:author | pubmed-author:WuHaonanH | lld:pubmed |
| pubmed-article:17553836 | pubmed:author | pubmed-author:WangWenkaiW | lld:pubmed |
| pubmed-article:17553836 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:17553836 | pubmed:volume | 35 | lld:pubmed |
| pubmed-article:17553836 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:17553836 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:17553836 | pubmed:pagination | W339-44 | lld:pubmed |
| pubmed-article:17553836 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
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| pubmed-article:17553836 | pubmed:year | 2007 | lld:pubmed |
| pubmed-article:17553836 | pubmed:articleTitle | MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. | lld:pubmed |
| pubmed-article:17553836 | pubmed:affiliation | State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China. | lld:pubmed |
| pubmed-article:17553836 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:17553836 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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