|
rdf:type |
|
|
lifeskim:mentions |
|
|
pubmed:dateCreated |
2011-2-15
|
|
pubmed:abstractText |
Molecular classification of tumors can be achieved by global gene expression profiling. Most machine learning classification algorithms furnish global error rates for the entire population. A few algorithms provide an estimate of probability of malignancy for each queried patient but the degree of accuracy of these estimates is unknown. On the other hand local minimax learning provides such probability estimates with best finite sample bounds on expected mean squared error on an individual basis for each queried patient. This allows a significant percentage of the patients to be identified as confidently predictable, a condition that ensures that the machine learning algorithm possesses an error rate below the tolerable level when applied to the confidently predictable patients.
|
|
pubmed:grant |
|
|
pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-10521349,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-11742071,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-11823860,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-14722351,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-15130820,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-15591335,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-16105897,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-16273092,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-16761367,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-18385729,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-18385730,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21261972-20676074
|
|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:status |
MEDLINE
|
|
pubmed:issn |
1755-8794
|
|
pubmed:author |
|
|
pubmed:issnType |
Electronic
|
|
pubmed:volume |
4
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
10
|
|
pubmed:dateRevised |
2011-7-25
|
|
pubmed:meshHeading |
|
|
pubmed:year |
2011
|
|
pubmed:articleTitle |
Confident predictability: identifying reliable gene expression patterns for individualized tumor classification using a local minimax kernel algorithm.
|
|
pubmed:affiliation |
Department of Mathematical Sciences, University of Massachusetts, Lowell, MA, USA. Lee_Jones@UML.edu
|
|
pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, N.I.H., Extramural
|
