The project aims to predict gene expression by analyzing millions of random promoter sequences, addressing challenges such as class imbalance and comparing the efficacy of convolution-based and attention-based networks in capturing local patterns.
The goal of this project is to predict prostate cancer by implementing the learning pipeline with self-supervised learning and transfer learning.
