| pubmed-article:20840733 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:20840733 | lifeskim:mentions | umls-concept:C1704675 | lld:lifeskim |
| pubmed-article:20840733 | lifeskim:mentions | umls-concept:C0019409 | lld:lifeskim |
| pubmed-article:20840733 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
| pubmed-article:20840733 | lifeskim:mentions | umls-concept:C0282173 | lld:lifeskim |
| pubmed-article:20840733 | lifeskim:mentions | umls-concept:C0205460 | lld:lifeskim |
| pubmed-article:20840733 | pubmed:dateCreated | 2010-9-15 | lld:pubmed |
| pubmed-article:20840733 | pubmed:abstractText | Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. | lld:pubmed |
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| pubmed-article:20840733 | pubmed:language | eng | lld:pubmed |
| pubmed-article:20840733 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20840733 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:20840733 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:20840733 | pubmed:issn | 1752-0509 | lld:pubmed |
| pubmed-article:20840733 | pubmed:author | pubmed-author:XiaZhengZ | lld:pubmed |
| pubmed-article:20840733 | pubmed:author | pubmed-author:ZhouXiaoboX | lld:pubmed |
| pubmed-article:20840733 | pubmed:author | pubmed-author:WongStephen... | lld:pubmed |
| pubmed-article:20840733 | pubmed:author | pubmed-author:WuLing-YunLY | lld:pubmed |
| pubmed-article:20840733 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:20840733 | pubmed:volume | 4 Suppl 2 | lld:pubmed |
| pubmed-article:20840733 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:20840733 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:20840733 | pubmed:pagination | S6 | lld:pubmed |
| pubmed-article:20840733 | pubmed:dateRevised | 2011-7-20 | lld:pubmed |
| pubmed-article:20840733 | pubmed:meshHeading | pubmed-meshheading:20840733... | lld:pubmed |
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| pubmed-article:20840733 | pubmed:meshHeading | pubmed-meshheading:20840733... | lld:pubmed |
| pubmed-article:20840733 | pubmed:meshHeading | pubmed-meshheading:20840733... | lld:pubmed |
| pubmed-article:20840733 | pubmed:meshHeading | pubmed-meshheading:20840733... | lld:pubmed |
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| pubmed-article:20840733 | pubmed:meshHeading | pubmed-meshheading:20840733... | lld:pubmed |
| pubmed-article:20840733 | pubmed:year | 2010 | lld:pubmed |
| pubmed-article:20840733 | pubmed:articleTitle | Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. | lld:pubmed |
| pubmed-article:20840733 | pubmed:affiliation | Bioinformatics and Bioengineering Program, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA. zxia@tmhs.org | lld:pubmed |
| pubmed-article:20840733 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:20840733 | pubmed:publicationType | Validation Studies | lld:pubmed |
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