Source:http://linkedlifedata.com/resource/pubmed/id/20005686
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2-3
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| pubmed:dateCreated |
2010-2-22
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| pubmed:abstractText |
Accurate cancer prognosis prediction is critical to cancer treatment. There have been many prognosis models based on clinical markers, but few of them are satisfied in clinical applications. And with the development of microarray technologies, cancer researchers have discovered many genes as new markers from the gene expression data and have further developed powerful prognosis models based on these so-called genetic biomarkers. However, the application of such biomarkers still suffers from some problems. The first one is there are a great number of genes and a few samples in the gene expression data so that it is difficult to select a unified gene set to establish a stable classifier for prognosis. The second one is that, due to the experimental and technical reasons, there are existing noises and redundancies in gene expression data, which may lead to building a prognosis predictor with poor performance. The last but not the least one is the microarray experiments are so expensive currently that it is hard to obtain abundant samples. Therefore, it is practical to develop prognosis methods mainly based on conventional clinical markers in real cancer treatment applications. This paper aims to establish an accurate classification model for cancer prognosis, in order to make full use of the invaluable information in clinical data, especially which is usually ignored by most of the existing methods when they aim for high prediction accuracies.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:chemical | |
| pubmed:status |
MEDLINE
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| pubmed:issn |
1873-2860
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| pubmed:author | |
| pubmed:copyrightInfo |
2009 Elsevier B.V. All rights reserved.
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| pubmed:issnType |
Electronic
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| pubmed:volume |
48
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
129-37
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| pubmed:meshHeading |
pubmed-meshheading:20005686-Artificial Intelligence,
pubmed-meshheading:20005686-Breast Neoplasms,
pubmed-meshheading:20005686-Data Mining,
pubmed-meshheading:20005686-Databases, Genetic,
pubmed-meshheading:20005686-Female,
pubmed-meshheading:20005686-Gene Expression Profiling,
pubmed-meshheading:20005686-Gene Expression Regulation, Neoplastic,
pubmed-meshheading:20005686-Genetic Markers,
pubmed-meshheading:20005686-Humans,
pubmed-meshheading:20005686-Models, Biological,
pubmed-meshheading:20005686-Models, Statistical,
pubmed-meshheading:20005686-Predictive Value of Tests,
pubmed-meshheading:20005686-Prognosis,
pubmed-meshheading:20005686-Reproducibility of Results,
pubmed-meshheading:20005686-Systems Biology,
pubmed-meshheading:20005686-Systems Integration,
pubmed-meshheading:20005686-Tumor Markers, Biological
|
| pubmed:articleTitle |
Mixture classification model based on clinical markers for breast cancer prognosis.
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| pubmed:affiliation |
School of Computer, Wuhan University, Wuhan 430079, China.
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|