Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

By Wouter G. Touw, Jumamurat R. Bayjanov, Lex Overmars, Lennart Backus, Jos Boekhorst, Michiel Wels, Sacha A. F. T. van Hijum. Briefings Bioinform., 2012

426 citations recorded by the source corpus.

In the Life Sciences ‘omics’ data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. 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. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. 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. 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. 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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/448c7ee9f262619b0277f94eda5a466aa539908b

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Citations in the corpus

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins (2016, methodology): “We chose the Random Forest classifier because it is nonlinear, nonparametric, regularized and simple to train (152, 153).”
Understanding Random Forests: From Theory to Practice (2014, background): “Some of them are reviewed in [Geurts et al., 2009; Touw et al., 2013; Boulesteix et al., 2012].”
Deep learning of the tissue-regulated splicing code (2014, methodology): “Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions.”
Robustness of Random Forest-based gene selection methods (2013, methodology): “The Random Forest algorithm is popular in the life sciences because it supports p n datasets, is robust to large amounts of noise, requires little parameter tuning and requires no predictor transformation [3-6].”