Random Forests for Genomic Data Analysis

By X. Chen, Hemant Ishwaran. Genomics, 2012

867 citations recorded by the source corpus.

Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly data adaptive, applies to “large p, small n” problems, and is able to account for correlation as well as interactions among features. This makes RF particularly appealing for high-dimensional genomic data analysis. In this article, we systematically review the applications and recent progresses of RF for genomic data, including prediction and classification, variable selection, pathway analysis, genetic association and epistasis detection, and unsupervised learning.

Subfields: Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8cc0c97b34a1351338cb8bf206a661851f3f135d

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Citations in the corpus

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “, Chen and Ishwaran [12]), quantitative structure−activity relationships (QSARs) modeling [13], and single nucleotide polymorphism studies (SNP, [14]).”
Exosome transfer from stromal to breast cancer cells regulates therapy resistance pathways. (2014, background): “to Breast Cancer Cells Regulates”
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence (2019, background): “Random forests have generated state of the art results in a number of quantitative disciplines, including genomics (Chen and Ishwaran 2012), econometrics (Mullainathan and Spiess 2017), and computational linguistics (Kontonatsios et al. 2014).”
Radiation and Dual Checkpoint Blockade Activates Non-Redundant Immune Mechanisms in Cancer (2015, neutral): “Random forest (RF) for classification, regression, and survival analysis is a multivariable non-parametric ensemble partitioning tree method that can be used to model the effect of all interactions between genes on a response variable 30,31 .”
Machine Learning for Precision Medicine. (2020, methodology): “…of variables and considerably fewer observations, this 15 machine learning model has been applied to various other of genomic data analysis 16 tasks in addition to classification and feature selection such as pathway analysis, 17 genetic association, and epistasis detection (Chen & Ishwaran, 2012).”
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, background): “scientific context [7], (ii) genomics research [39] and”
A random forest based approach for predicting spreads in the primary catastrophe bond market (2020, methodology): “Although one could use other approaches to select variables, outside the scope of minimal depth, see Chen & Ishwaran (2012) for an overview, it is not a necessity here as our data set is not high dimensional, i.e.”
A review on longitudinal data analysis with random forest (2022, neutral): “Moreover, it can work with predictors of various scales or distributions and is suited for applications in high-dimensional settings where the number of predictors can be larger than the number of observations [12, 13].”
Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “RF extends the idea of DTs, a nonparametric tree-based method that uses bootstrap sampling to build an ensemble of DT classifiers and predicts the outcome by aggregate voting from all DTs [16, 17].”
The Consensus Molecular Subtypes of Colorectal Cancer (2015, methodology): “is supported by grants from the Dutch Cancer Society (UVA2011-4969, UVA2014-7245), Worldwide Cancer Research (14-1164), the Maag Lever Darm Stichting (MLDS-CDG 14-03) and the European Research Council (ERG-StG 638193).”