1. Introduction

1. Introduction

Last modified by rflseo on 2017/02/18 21:06

Regulome Explorer: Random Forest allows the exploration of multivariate associations within a heterogeneous dataset containing clinical, sample, and molecular data for hundreds of patients.  Multivariate associations across molecular and clinical features of these datasets were uncovered using RF-ACE, a novel Random Forest (RF) based feature selection algorithm. RF-ACE assesses statistical significance of each uncovered association with a two-sample t-test, where the null hypothesis is derived empirically via a permutation approach. Thus, each association receives a p-value, permitting direct comparison of statistical strengths between uncovered associations in the network.

For more information about RF-ACE, see

Created by Lisa on 2012/11/26 13:12

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