Random forests for genomic data analysis

Exploratory Data Analysis using Random Forests

Although the rise of "big data" has made machine learning algorithms more visible and relevant for social scientists, they are still widely considered to be "black box" models that are not well suited for substantive research: only prediction. We argue that this need not be the case, and present one method, Random Forests, with an emphasis on its practical application for exploratory analysis a...

Analysis of missing data with random forests

Random Forests for Big Data

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include data streams and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based o...

Random survival forests for high-dimensional data

Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problems using Random Survival Forests (RSF), a new extension of Breiman’s Random Forests (RF) to survival settings. We review this methodology and demonstrate its use in high-dimensional survival problems using a public domai...

A genomic random interval model for statistical analysis of genomic lesion data

MOTIVATION Tumors exhibit numerous genomic lesions such as copy number variations, structural variations and sequence variations. It is difficult to determine whether a specific constellation of lesions observed across a cohort of multiple tumors provides statistically significant evidence that the lesions target a set of genes that may be located across different chromosomes but yet are all in...