This project implements the experimental pipeline for learning and analyzing the differentially expressed gene model. Click here to see a full report with background explanation, experimental setup, and description of the findings obtained as a result of this analysis.

The project uses primarily python code. The model is based on convolutional neural nets, with EBLEARN being the 3rd party library containing the actual learning algorithm (http://eblearn.sourceforge.net/). The code which calls the EBLEARN library and learns/tests the CNN model is written in C++. The rest of the code for data pre-processing and model visualization is written in python.