CancerDiscover: a GUI for cancer prediction and biometric identification using microarray data

Problem

As of today, CancerDiscover has a not-so-easy build and workflow for users to conduct microarray experiments. Moreover, the current setup requires users to manually download datasets and at the same time, separately label them. Users have no visual analysis provided at any given time during the course of the experiment. Moreover, users are also limited to use the default parameters for each classifier, thus limiting a user to build more efficient prediction models. Finally, the overall workflow is poorly documented for installation and usage.

Solution

candis - A minimalistic clean Graphical User Interface integrated with the current command-line tool will not only provide a smooth build and workflow for an experiment setup, but also provide remote downloading for data sets, quality control visualisations and user-defined parameters during analysis. A modular framework will also provide future extensions for more techniques. Users will also have access to a well-documented manual that eases the overall use of the proposed software. The current ongoing development of this application can be viewed at github.com/achillesrasquinha/candis