| pubmed-article:18048187 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18048187 | lifeskim:mentions | umls-concept:C0043393 | lld:lifeskim |
| pubmed-article:18048187 | lifeskim:mentions | umls-concept:C0017337 | lld:lifeskim |
| pubmed-article:18048187 | lifeskim:mentions | umls-concept:C0205245 | lld:lifeskim |
| pubmed-article:18048187 | lifeskim:mentions | umls-concept:C0023745 | lld:lifeskim |
| pubmed-article:18048187 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
| pubmed-article:18048187 | lifeskim:mentions | umls-concept:C1709016 | lld:lifeskim |
| pubmed-article:18048187 | pubmed:issue | 2 | lld:pubmed |
| pubmed-article:18048187 | pubmed:dateCreated | 2007-11-30 | lld:pubmed |
| pubmed-article:18048187 | pubmed:abstractText | Understanding how genes are functionally related requires efficient algorithms to model networks from expression data. We report a heuristic search algorithm called Two-Level Simulated Annealing (TLSA) that is more likely to find the global optimal network structure compared to conventional simulated annealing and other searching schemes. We have applied this method to search for a global optimised network structure from a synthetic data set and an expression data set of S. cerevisiae mutants. We have achieved better precision and recall compared to other searching algorithms and are able to map relationships more accurately among functionally-linked genes. | lld:pubmed |
| pubmed-article:18048187 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18048187 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18048187 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18048187 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18048187 | pubmed:issn | 1744-5485 | lld:pubmed |
| pubmed-article:18048187 | pubmed:author | pubmed-author:WaddW BWB | lld:pubmed |
| pubmed-article:18048187 | pubmed:author | pubmed-author:TouchmanJeffr... | lld:pubmed |
| pubmed-article:18048187 | pubmed:author | pubmed-author:XueGuoliangG | lld:pubmed |
| pubmed-article:18048187 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:18048187 | pubmed:volume | 3 | lld:pubmed |
| pubmed-article:18048187 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18048187 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18048187 | pubmed:pagination | 170-86 | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:meshHeading | pubmed-meshheading:18048187... | lld:pubmed |
| pubmed-article:18048187 | pubmed:year | 2007 | lld:pubmed |
| pubmed-article:18048187 | pubmed:articleTitle | Modelling gene functional linkages using yeast microarray data. | lld:pubmed |
| pubmed-article:18048187 | pubmed:affiliation | Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona 85287, USA. tie.wang@asu.edu | lld:pubmed |
| pubmed-article:18048187 | pubmed:publicationType | Journal Article | lld:pubmed |
