Abstract
Bioinformatics [1] is now one of the fastest growing and most promising interdisciplinary fields of science, providing the methods and software tools to get insights into information processing in living organisms. This field tries to apply well-established methods from various domains but also develop new algorithms that enable a better understanding of biological data. With the advent of high throughput technologies, it is possible to obtain huge amounts of genomic data in a relatively easy and cheap way. Among the many types of omics data, gene expression data are the most readily analyzed as expression profiles and are perceived as extremely useful in a precise diagnosis (e.g., cancer subtype differentiation) and personalized treatment.
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Ensemble methods use multiple learning algorithms (so-called committees of classifiers) to obtain better predictive performance, and decision trees are very popular component classifiers.
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Kretowski, M. (2019). Multi-test Decision Trees for Gene Expression Data. In: Evolutionary Decision Trees in Large-Scale Data Mining. Studies in Big Data, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-030-21851-5_7
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DOI: https://doi.org/10.1007/978-3-030-21851-5_7
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