Rank-based classifiers for extremely high-dimensional gene expression data
| Autor: | Ludwig Lausser, Adalbert F. X. Wilhelm, Florian Schmid, Lyn-Rouven Schirra, Hans A. Kestler |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Statistics and Probability Clustering high-dimensional data Basis (linear algebra) business.industry Applied Mathematics Rank (computer programming) Ordinal Scale Pattern recognition computer.software_genre 01 natural sciences Expression (mathematics) Computer Science Applications Random forest Support vector machine 010104 statistics & probability 03 medical and health sciences 030104 developmental biology Transformation (function) Artificial intelligence Data mining 0101 mathematics business computer Mathematics |
| Zdroj: | Advances in Data Analysis and Classification. 12:917-936 |
| ISSN: | 1862-5355 1862-5347 |
| DOI: | 10.1007/s11634-016-0277-3 |
| Popis: | Predicting phenotypes on the basis of gene expression profiles is a classification task that is becoming increasingly important in the field of precision medicine. Although these expression signals are real-valued, it is questionable if they can be analyzed on an interval scale. As with many biological signals their influence on e.g. protein levels is usually non-linear and thus can be misinterpreted. In this article we study gene expression profiles with up to 54,000 dimensions. We analyze these measurements on an ordinal scale by replacing the real-valued profiles by their ranks. This type of rank transformation can be used for the construction of invariant classifiers that are not affected by noise induced by data transformations which can occur in the measurement setup. Our 10 $$\times $$ 10 fold cross-validation experiments on 86 different data sets and 19 different classification models indicate that classifiers largely benefit from this transformation. Especially random forests and support vector machines achieve improved classification results on a significant majority of datasets. |
| Databáze: | OpenAIRE |
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