This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model.
For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. SNPs in genetic association studies) to separate different classes (e.g. Classification techniques allow training a model based on variables (e.g. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. In the Life Sciences ‘omics’ data is increasingly generated by different high-throughput technologies.
0 kommentar(er)
