22.5.2019 – PROMO 2019.5.1 is here!

        Note: Requires Matlab 9.6 (2019a) Runtime Environment (Download)

What's new ?

-        Download and import of GEO series files directly from PROMO

-        Download and import GEO GPL files (enabling adding gene symbols for currently loaded GEO genomic datasets).

24.4.2019 – PROMO 2019.1.1 is here!

What's new ?

-        Rank genes by correlation to a given gene symbols

-        Rank genes by survival prediction (Based on COX regression analysis)

-        Pie graph for visualizing label distribution

-        Edit dataset title

Note: This is the last version to support Matlab's 2016a (9.0) runtime environment! Next version will run only on 2019a (9.6) runtime environment.

22.1.2019 – PROMO 2019.1.0 is here!

What's new ?

-        Various improvements and bug fixes

-        Gene ontology enrichment analysis on gene clusters

-        PROMO's dataset compilation page is available at http://acgt.cs.tau.ac.il/promo/datasets/

-        Guess K feature

-        Automatic generation of decision tree classifier for any selected label

5.7.2018 – PROMO 2018.1.1 is here !

What's new ?

-        Various improvements and bug fixes

31.12.2017 - PROMO 2017.1.2 is out and running!

New Features include:

-     Filter features by Id – enables feature filtering by specifying the features using the Feature Selection window. 

-     Features are filtered at display level only. If you wish to use only the remaining features for analysis purposes, this can be done using the Data menu: Data -> Make gene view prime data

-     Help menu added, containing a tutorial link and technical log support.


Bug fixes:

-     Sort samples by features with only one feature shown – fixed

-     Consensus Clustering fails when specifying an internal k – fixed

-     Error occurred while trying to intersect a partial list of the DSC's datasets - fixed


PROMO 2017.1.1 is out and running!

What's new ?

-     Dataset collection management – allows you to build, edit and save a collection of datasets.

-     Dataset collection integrative functions: intersection and merge of several datasets.

-     Correlation analysis of features belonging to 2 different datasets.

-     Multi omic clustering – allows you to cluster a number of omics altogether using different clustering methods.