MOCA (Multivariate Organization of Combinatorial Alterations)

is a non-parametric algorithm to discover constellations of dichotomous features that predict a phenotype of interest with high sensitivity and specficity. Current release is MOCA 0.80, last updated on 09/21/2016.

MOCA software is intended for those with substantial bioinformatics and Python expertise.

MOCA is free for non-commercial use. Commercial users should contact the Johns Hopkins Technology Transfer office

Documentation for the developer

Download MOCA source code from GitHub

For documentation details, please contact the primary developer.

Primary citations

If you use MOCA software for a publication, please cite the following:

Masica DL and Karchin R (2013) Collections of simultaneously altered genes as biomarkers of cancer cell drug response Cancer Research. 73(6):1699-708

Masica DL and Karchin R (2011) Correlation among somatic mutation expression identifies genes important in human glioblastoma progression and survival Cancer Research. 71(13):4550-61

Software primary contact/developer

David Masica  david dot masica at gmail dot com

This is a beta version of the MOCA documentation page. Please contact the primary developer with feedback, suggestions, and requests to improve this page.