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.