Gene Expression Prediction Using Deep Learning

The project involves predicting gene expression through the analysis of millions of random promoter sequences. A key challenge addressed in the project is class imbalance. The research includes experiments designed to capture local patterns within the data and evaluates the performance of both convolution-based and attention-based networks. This comprehensive approach aims to enhance our understanding of gene expression prediction and contribute insights into the effectiveness of different neural network architectures in addressing this biological challenge.