Classification of microarrays; synergistic effects between normalization, gene selection and machine learning

Background Machine learning is a powerful approach for describing and predicting classes in microarray data. Although several comparative studies have investigated the relative performance of various machine learning methods, these often do not account for the fact that performance (e.g. error rate)...

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Vydáno v:	BMC bioinformatics Ročník 12; číslo 1; s. 390
Hlavní autoři:	Önskog, Jenny, Freyhult, Eva, Landfors, Mattias, Rydén, Patrik, Hvidsten, Torgeir R
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 07.10.2011 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Artificial Intelligence Bioinformatics Biomedical and Life Sciences Cancer cell Classification Comparative genomics Comparative studies Computational Biology/Bioinformatics Computer Appl. in Life Sciences DNA microarrays expression features Gene expression Gene Expression Profiling Genetic aspects Humans Indexing in process Life Sciences Microarrays Neoplasms - genetics Oligonucleotide Array Sequence Analysis Research Article Software statistical methods Studies Support Vector Machine Synergistic effect tumors Variables Sweden Machine Learning Method Support Vector Machine Gene Selection Background Correction Hide Layer
ISSN:	1471-2105, 1471-2105
On-line přístup:	Získat plný text
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