| pubmed-article:15963230 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C0033684 | lld:lifeskim |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C0439064 | lld:lifeskim |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C0475264 | lld:lifeskim |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C1705938 | lld:lifeskim |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C1527178 | lld:lifeskim |
| pubmed-article:15963230 | lifeskim:mentions | umls-concept:C0871161 | lld:lifeskim |
| pubmed-article:15963230 | pubmed:dateCreated | 2005-8-4 | lld:pubmed |
| pubmed-article:15963230 | pubmed:abstractText | Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively. | lld:pubmed |
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| pubmed-article:15963230 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:15963230 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:15963230 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:15963230 | pubmed:issn | 1471-2105 | lld:pubmed |
| pubmed-article:15963230 | pubmed:author | pubmed-author:LiKuo-BinKB | lld:pubmed |
| pubmed-article:15963230 | pubmed:author | pubmed-author:KrishnanArunA | lld:pubmed |
| pubmed-article:15963230 | pubmed:author | pubmed-author:SardaDeepakD | lld:pubmed |
| pubmed-article:15963230 | pubmed:author | pubmed-author:ChuaGek... | lld:pubmed |
| pubmed-article:15963230 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:15963230 | pubmed:volume | 6 | lld:pubmed |
| pubmed-article:15963230 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:15963230 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:15963230 | pubmed:pagination | 152 | lld:pubmed |
| pubmed-article:15963230 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
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| pubmed-article:15963230 | pubmed:meshHeading | pubmed-meshheading:15963230... | lld:pubmed |
| pubmed-article:15963230 | pubmed:year | 2005 | lld:pubmed |
| pubmed-article:15963230 | pubmed:articleTitle | pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. | lld:pubmed |
| pubmed-article:15963230 | pubmed:affiliation | Bioinformatics Institute, 30, Biopolis Street, #07-01, 138671, Singapore. deepak@bii.a-star.edu.sg | lld:pubmed |
| pubmed-article:15963230 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:15963230 | pubmed:publicationType | Validation Studies | lld:pubmed |
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