Regulome Explorer

This web-based tool provides for the exploration of data from TCGA. It can be used to explore the following:
  • The cancer comparison data sets which give information about common gene disruptions across different cancers.
  • Associations among multiple types of features (e.g., gene expression, methylation, copy number variation, clinical), computed using random forest regression and displayed within an interactive Circvis tool.
  • Individual genome aberrations identified through the analysis of structural variations using FastBreak.
  • Read more about Regulome Explorer in its open source project.

Colorectal Cancer Aggressiveness Explorer

The CRC Aggressiveness Explorer allows the exploration of molecular signatures associated with aggressive CRC. A red data point implies that the signature is more prevalent in tumors with aggressive colorectal cancer, while a blue data point indicates the opposite. A color gradient is used for the strength of association.


This application has been designed to quickly screen for structural variation. We have now expanded Fastbreak to begin looking for patterns of structural variation across cancer types. A paper covering the Fastbreak algorithm is submitted for publication.

Read more about Fastbreak in its open source project.


This program is an efficient C++ implementation of a robust machine learning algorithm for uncovering multivariate associations from large and diverse data sets. RF-ACE natively handles numerical and categorical data with missing values, and potentially large quantities of noninformative features.

Read more about RF-ACE in its open source project.