| pubmed-article:16403791 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16403791 | lifeskim:mentions | umls-concept:C0017428 | lld:lifeskim |
| pubmed-article:16403791 | lifeskim:mentions | umls-concept:C0011900 | lld:lifeskim |
| pubmed-article:16403791 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
| pubmed-article:16403791 | lifeskim:mentions | umls-concept:C1521991 | lld:lifeskim |
| pubmed-article:16403791 | lifeskim:mentions | umls-concept:C0008902 | lld:lifeskim |
| pubmed-article:16403791 | lifeskim:mentions | umls-concept:C0870951 | lld:lifeskim |
| pubmed-article:16403791 | pubmed:issue | 6 | lld:pubmed |
| pubmed-article:16403791 | pubmed:dateCreated | 2006-3-10 | lld:pubmed |
| pubmed-article:16403791 | pubmed:abstractText | Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. | lld:pubmed |
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| pubmed-article:16403791 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:16403791 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:16403791 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16403791 | pubmed:month | Mar | lld:pubmed |
| pubmed-article:16403791 | pubmed:issn | 1367-4803 | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:ClarkeRobertR | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:WangYueY | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:HoffmanEric... | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:BakayMarinaM | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:WangZuyiZ | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:XuanJianhuaJ | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:DongYibinY | lld:pubmed |
| pubmed-article:16403791 | pubmed:author | pubmed-author:FengYuanjianY | lld:pubmed |
| pubmed-article:16403791 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16403791 | pubmed:day | 15 | lld:pubmed |
| pubmed-article:16403791 | pubmed:volume | 22 | lld:pubmed |
| pubmed-article:16403791 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16403791 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16403791 | pubmed:pagination | 755-61 | lld:pubmed |
| pubmed-article:16403791 | pubmed:dateRevised | 2007-11-14 | lld:pubmed |
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| pubmed-article:16403791 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:16403791 | pubmed:articleTitle | Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data. | lld:pubmed |
| pubmed-article:16403791 | pubmed:affiliation | Center for Genetic Medicine, Children's National Medical Center Washington, DC 20010, USA. | lld:pubmed |
| pubmed-article:16403791 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16403791 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:16403791 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:16403791 | pubmed:publicationType | Evaluation Studies | lld:pubmed |
| pubmed-article:16403791 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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