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.