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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Vydané v:BMC bioinformatics Ročník 12; číslo 1; s. 390
Hlavní autori: Önskog, Jenny, Freyhult, Eva, Landfors, Mattias, Rydén, Patrik, Hvidsten, Torgeir R
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 07.10.2011
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
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
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ISSN:1471-2105, 1471-2105
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Popis
Shrnutí: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) is a result of a series of analysis steps of which the most important are data normalization, gene selection and machine learning. Results In this study, we used seven previously published cancer-related microarray data sets to compare the effects on classification performance of five normalization methods, three gene selection methods with 21 different numbers of selected genes and eight machine learning methods. Performance in term of error rate was rigorously estimated by repeatedly employing a double cross validation approach. Since performance varies greatly between data sets, we devised an analysis method that first compares methods within individual data sets and then visualizes the comparisons across data sets. We discovered both well performing individual methods and synergies between different methods. Conclusion Support Vector Machines with a radial basis kernel, linear kernel or polynomial kernel of degree 2 all performed consistently well across data sets. We show that there is a synergistic relationship between these methods and gene selection based on the T-test and the selection of a relatively high number of genes. Also, we find that these methods benefit significantly from using normalized data, although it is hard to draw general conclusions about the relative performance of different normalization procedures.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-12-390